Abstract

Glaucoma detection in color fundus images is a challenging task that requires expertise and years of practice. In this study we exploited the application of different Convolutional Neural Networks (CNN) schemes to show the influence in the performance of relevant factors like the data set size, the architecture and the use of transfer learning vs newly defined architectures. We also compared the performance of the CNN based system with respect to human evaluators and explored the influence of the integration of images and data collected from the clinical history of the patients. We accomplished the best performance using a transfer learning scheme with VGG19 achieving an AUC of 0.94 with sensitivity and specificity ratios similar to the expert evaluators of the study. The experimental results using three different data sets with 2313 images indicate that this solution can be a valuable option for the design of a computer aid system for the detection of glaucoma in large-scale screening programs.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
OSA Recommended Articles
Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images

Yu Wang, Yaonan Zhang, Zhaomin Yao, Ruixue Zhao, and Fengfeng Zhou
Biomed. Opt. Express 7(12) 4928-4940 (2016)

Fully automated diagnosis of papilledema through robust extraction of vascular patterns and ocular pathology from fundus photographs

Khush Naseeb Fatima, Taimur Hassan, M. Usman Akram, Mahmood Akhtar, and Wasi Haider Butt
Biomed. Opt. Express 8(2) 1005-1024 (2017)

Similarity regularized sparse group lasso for cup to disc ratio computation

Jun Cheng, Zhuo Zhang, Dacheng Tao, Damon Wing Kee Wong, Jiang Liu, Mani Baskaran, Tin Aung, and Tien Yin Wong
Biomed. Opt. Express 8(8) 3763-3777 (2017)

References

  • View by:
  • |
  • |
  • |

  1. R. N. Weinreb, T. Aung, and F. A. Medeiros, “The Pathophysiology and Treatment of Glaucoma: A Review,” JAMA 311(18), 1901–1911 (2014).
    [Crossref] [PubMed]
  2. Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C.-Y. Cheng, “Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis,” Ophthalmology 121(11), 2081–2090 (2014).
    [Crossref] [PubMed]
  3. J. M. Tielsch, J. Katz, K. Singh, H. A. Quigley, J. D. Gottsch, J. Javitt, and A. Sommer, “A Population-based Evaluation of Glaucoma Screening: the Baltimore Eye Survey,” Am. J. Epidemiol. 134(10), 1102–1110 (1991).
    [Crossref] [PubMed]
  4. C. Fleming, E. P. Whitlock, T. Beil, B. Smit, and R. P. Harris, “Screening for Primary Open-Angle Glaucoma in the Primary Care Setting: An Update for the US Preventive Services Task Force,” Ann. Fam. Med. 3(2), 167–170 (2005).
    [Crossref] [PubMed]
  5. E. A. Maul and H. D. Jampel, “Glaucoma Screening in the Real World,” Ophthalmology 117(9), 1665–1666 (2010).
    [Crossref] [PubMed]
  6. D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
    [Crossref] [PubMed]
  7. J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
    [Crossref] [PubMed]
  8. T. R. Einarson, C. Vicente, M. Machado, D. Covert, G. E. Trope, and M. Iskedjian, “Screening for glaucoma in Canada: a systematic review of the literature,” Can. J. Ophthalmol. 41(6), 709–721 (2006).
    [Crossref] [PubMed]
  9. A. M. Ervin, M. V. Boland, E. H. Myrowitz, J. Prince, B. Hawkins, D. Vollenweider, D. Ward, C. Suarez-Cuervo, and K. A. Robinson, “Screening for Glaucoma: Comparative Effectiveness,” D): Agency for Healthcare Research and Quality (US); 2012 Apr. Report No.: 12-EHC037-EF (2012).
  10. P. R. Healey, A. J. Lee, T. Aung, T. Y. Wong, and P. Mitchell, “Diagnostic Accuracy of the Heidelberg Retina Tomograph for Glaucoma A Population-Based Assessment,” Ophthalmology 117(9), 1667–1673 (2010).
    [Crossref] [PubMed]
  11. G. Li, A. K. Fansi, J.-F. Boivin, L. Joseph, and P. Harasymowycz, “Screening for Glaucoma in High-Risk Populations Using Optical Coherence Tomography,” Ophthalmology 117(3), 453–461 (2010).
    [Crossref] [PubMed]
  12. N. Yamada, P. P. Chen, R. P. Mills, M. M. Leen, R. L. Stamper, M. F. Lieberman, L. Xu, and D. C. Stanford, “Glaucoma screening using the scanning laser polarimeter,” J. Glaucoma 9(3), 254–261 (2000).
    [Crossref] [PubMed]
  13. P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
    [Crossref] [PubMed]
  14. M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
    [Crossref] [PubMed]
  15. M. S. Haleem, L. Han, J. van Hemert, and B. Li, “Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review,” Comput. Med. Imaging Graph. 37(7-8), 581–596 (2013).
    [Crossref] [PubMed]
  16. M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated Segmentation of the Optic Disc from Stereo Color Photographs Using Physiologically Plausible Features,” Invest. Ophthalmol. Vis. Sci. 48(4), 1665–1673 (2007).
    [Crossref] [PubMed]
  17. T. Walter and J.-C. Klein, “Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques,” in Medical Data Analysis, Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2001), pp. 282–287.
  18. M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, and A. Laude, “Computer-aided diagnosis of diabetic retinopathy: A review,” Comput. Biol. Med. 43(12), 2136–2155 (2013).
    [Crossref] [PubMed]
  19. R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14(3), 471–481 (2010).
    [Crossref] [PubMed]
  20. U. R. Acharya, S. Dua, X. Du, V. Sree S, and C. K. Chua, “Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features,” IEEE Trans. Inf. Technol. Biomed. 15(3), 449–455 (2011).
    [Crossref] [PubMed]
  21. M. M. R. Krishnan and O. Faust, “Automated glaucoma detection using hybrid feature extraction in retinal fundus images,” J. Mech. Med. Biol. 13(01), 1350011 (2013).
    [Crossref]
  22. M. R. K. Mookiah, U. Rajendra Acharya, C. M. Lim, A. Petznick, and J. S. Suri, “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features,” Knowl.- Based Syst. 33, 73–82 (2012).
    [Crossref]
  23. S. Maheshwari, R. B. Pachori, and U. R. Acharya, “Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images,” IEEE J. Biomed. Health Inform. 21(3), 803–813 (2017).
    [Crossref] [PubMed]
  24. U. R. Acharya, S. Bhat, J. E. W. Koh, S. V. Bhandary, and H. Adeli, “A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images,” Comput. Biol. Med. 88, 72–83 (2017).
    [Crossref] [PubMed]
  25. K. Fukushima and S. Miyake, “Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition,” in Competition and Cooperation in Neural Nets, Lecture Notes in Biomathematics (Springer, Berlin, Heidelberg, 1982), pp. 267–285.
  26. G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
    [Crossref] [PubMed]
  27. H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
    [Crossref] [PubMed]
  28. S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
    [Crossref]
  29. J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 248–255.
    [Crossref]
  30. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
    [Crossref]
  31. S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8(2), 579–592 (2017).
    [Crossref] [PubMed]
  32. B. Q. Huynh, H. Li, and M. L. Giger, “Digital mammographic tumor classification using transfer learning from deep convolutional neural networks,” J. Med. Imaging (Bellingham) 3(3), 034501 (2016).
    [Crossref] [PubMed]
  33. N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and Jianming Liang, “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016).
    [Crossref] [PubMed]
  34. H. Fu, Y. Xu, S. Lin, D. W. K. Wong, and J. Liu, “DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Lecture Notes in Computer Science (Springer, Cham, 2016), pp. 132–139.
  35. D. Mahapatra, P. K. Roy, S. Sedai, and R. Garnavi, “Retinal Image Quality Classification Using Saliency Maps and CNNs,” in Machine Learning in Medical Imaging, Lecture Notes in Computer Science (Springer, Cham, 2016), pp. 172–179.
  36. J. Zilly, J. M. Buhmann, and D. Mahapatra, “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation,” Comput. Med. Imaging Graph. 55, 28–41 (2017).
    [Crossref] [PubMed]
  37. A. Sevastopolsky, “Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network,” Pattern Recognit. Image Anal. 27(3), 618–624 (2017).
    [Crossref]
  38. M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
    [Crossref] [PubMed]
  39. M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images,” IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016).
    [Crossref] [PubMed]
  40. X. Chen, Y. Xu, D. W. K. Wong, T. Y. Wong, and J. Liu, “Glaucoma detection based on deep convolutional neural network,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2015), pp. 715–718.
    [Crossref]
  41. B. Al-Bander, W. Al-Nuaimy, M. A. Al-Taee, and Y. Zheng, “Automated glaucoma diagnosis using deep learning approach,” in 2017 14th International Multi-Conference on Systems, Signals Devices (SSD) (2017), pp. 207–210.
  42. H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, and X. Cao, “Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image,” IEEE Trans. Med. Imaging 37(11), 2493–2501 (2018).
    [Crossref] [PubMed]
  43. M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
    [Crossref] [PubMed]
  44. Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs,” Ophthalmology 125(8), 1199–1206 (2018).
    [Crossref] [PubMed]
  45. N. Shibata, M. Tanito, K. Mitsuhashi, Y. Fujino, M. Matsuura, H. Murata, and R. Asaoka, “Development of a deep residual learning algorithm to screen for glaucoma from fundus photography,” Sci. Rep. 8(1), 14665 (2018).
    [Crossref] [PubMed]
  46. H. Muhammad, T. J. Fuchs, N. De Cuir, C. G. De Moraes, D. M. Blumberg, J. M. Liebmann, R. Ritch, and D. C. Hood, “Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects,” https://www.ingentaconnect.com/content/wk/jglau/2017/00000026/00000012/art00008 .
    [Crossref]
  47. K. Gopinath, S. B. Rangrej, and J. Sivaswamy, “A deep learning framework for segmentation of retinal layers from OCT images,” ArXiv180608859 Cs (2018).
  48. L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
    [Crossref] [PubMed]
  49. F. Fumero, S. Alayon, J. L. Sanchez, J. Sigut, and M. Gonzalez-Hernandez, “RIM-ONE: An open retinal image database for optic nerve evaluation,” in 2011 24th International Symposium on Computer-Based Medical Systems (CBMS) (2011), pp. 1–6.
    [Crossref]
  50. J. Sivaswamy, S. R. Krishnadas, G. D. Joshi, M. Jain, and A. U. S. Tabish, “Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation,” in 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) (2014), pp. 53–56.
    [Crossref]
  51. S. Sekhar, W. Al-Nuaimy, and A. K. Nandi, “Automated localisation of optic disk and fovea in retinal fundus images,” in 2008 16th European Signal Processing Conference (2008), pp. 1–5.
  52. S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” ArXiv150203167 Cs (2015).
  53. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” ArXiv14091556 Cs (2014).
  54. N. Antropova, B. Q. Huynh, and M. L. Giger, “A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets,” Med. Phys. 44(10), 5162–5171 (2017).
    [Crossref] [PubMed]
  55. H. Liao, “A Deep Learning Approach to Universal Skin Disease Classification,” 8 (2016).
  56. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going Deeper With Convolutions,” in (2015), pp. 1–9.
  57. B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
    [Crossref] [PubMed]
  58. S. Weng, X. Xu, J. Li, and S. T. C. Wong, “Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer,” J. Biomed. Opt. 22(10), 1–10 (2017).
    [Crossref] [PubMed]
  59. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” ArXiv151203385 Cs (2015).
  60. H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation,” IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018).
    [Crossref] [PubMed]
  61. T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
    [Crossref]
  62. R. Fluss, D. Faraggi, and B. Reiser, “Estimation of the Youden Index and its Associated Cutoff Point,” Biom. J. 47(4), 458–472 (2005).
    [Crossref] [PubMed]
  63. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).
  64. Sander Dieleman, Jan Schlüter, Colin Raffel, Eben Olson, Søren Kaae Sønderby, Daniel Nouri, Daniel Maturana, Martin Thoma, Eric Battenberg, Jack Kelly, Jeffrey De Fauw, Michael Heilman, diogo149, Brian McFee, Hendrik Weideman, takacsg84, peterderivaz, Jon, instagibbs, Dr. Kashif Rasul, CongLiu, Britefury, and Jonas Degrave, Lasagne: First Release. (Zenodo, 2015).
  65. F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio, “Theano: new features and speed improvements,” ArXiv12115590 Cs (2012).
  66. V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
    [Crossref] [PubMed]
  67. T. Xu, H. Zhang, X. Huang, S. Zhang, and D. N. Metaxas, “Multimodal Deep Learning for Cervical Dysplasia Diagnosis,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Lecture Notes in Computer Science (Springer, Cham, 2016), pp. 115–123.
  68. S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
    [Crossref] [PubMed]
  69. P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. Velázquez Vega, D. J. Brat, and L. A. D. Cooper, “Predicting cancer outcomes from histology and genomics using convolutional networks,” Proc. Natl. Acad. Sci. U.S.A. 115(13), E2970–E2979 (2018).
    [Crossref] [PubMed]
  70. A. L. Coleman and S. Miglior, “Risk Factors for Glaucoma Onset and Progression,” Surv. Ophthalmol. 53(6Suppl1), S3–S10 (2008).
    [Crossref] [PubMed]
  71. M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” in Computer Vision – ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, eds., Lecture Notes in Computer Science (Springer International Publishing, 2014), pp. 818–833.
  72. A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
    [Crossref] [PubMed]

2018 (6)

H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, and X. Cao, “Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image,” IEEE Trans. Med. Imaging 37(11), 2493–2501 (2018).
[Crossref] [PubMed]

M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
[Crossref] [PubMed]

Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs,” Ophthalmology 125(8), 1199–1206 (2018).
[Crossref] [PubMed]

N. Shibata, M. Tanito, K. Mitsuhashi, Y. Fujino, M. Matsuura, H. Murata, and R. Asaoka, “Development of a deep residual learning algorithm to screen for glaucoma from fundus photography,” Sci. Rep. 8(1), 14665 (2018).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation,” IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018).
[Crossref] [PubMed]

P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. Velázquez Vega, D. J. Brat, and L. A. D. Cooper, “Predicting cancer outcomes from histology and genomics using convolutional networks,” Proc. Natl. Acad. Sci. U.S.A. 115(13), E2970–E2979 (2018).
[Crossref] [PubMed]

2017 (13)

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

J. Zilly, J. M. Buhmann, and D. Mahapatra, “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation,” Comput. Med. Imaging Graph. 55, 28–41 (2017).
[Crossref] [PubMed]

A. Sevastopolsky, “Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network,” Pattern Recognit. Image Anal. 27(3), 618–624 (2017).
[Crossref]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref] [PubMed]

N. Antropova, B. Q. Huynh, and M. L. Giger, “A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets,” Med. Phys. 44(10), 5162–5171 (2017).
[Crossref] [PubMed]

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

S. Weng, X. Xu, J. Li, and S. T. C. Wong, “Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer,” J. Biomed. Opt. 22(10), 1–10 (2017).
[Crossref] [PubMed]

S. Maheshwari, R. B. Pachori, and U. R. Acharya, “Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images,” IEEE J. Biomed. Health Inform. 21(3), 803–813 (2017).
[Crossref] [PubMed]

U. R. Acharya, S. Bhat, J. E. W. Koh, S. V. Bhandary, and H. Adeli, “A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images,” Comput. Biol. Med. 88, 72–83 (2017).
[Crossref] [PubMed]

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8(2), 579–592 (2017).
[Crossref] [PubMed]

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
[Crossref] [PubMed]

2016 (6)

B. Q. Huynh, H. Li, and M. L. Giger, “Digital mammographic tumor classification using transfer learning from deep convolutional neural networks,” J. Med. Imaging (Bellingham) 3(3), 034501 (2016).
[Crossref] [PubMed]

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and Jianming Liang, “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016).
[Crossref] [PubMed]

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images,” IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016).
[Crossref] [PubMed]

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

2015 (2)

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
[Crossref] [PubMed]

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

2014 (3)

R. N. Weinreb, T. Aung, and F. A. Medeiros, “The Pathophysiology and Treatment of Glaucoma: A Review,” JAMA 311(18), 1901–1911 (2014).
[Crossref] [PubMed]

Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C.-Y. Cheng, “Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis,” Ophthalmology 121(11), 2081–2090 (2014).
[Crossref] [PubMed]

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

2013 (3)

M. S. Haleem, L. Han, J. van Hemert, and B. Li, “Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review,” Comput. Med. Imaging Graph. 37(7-8), 581–596 (2013).
[Crossref] [PubMed]

M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, and A. Laude, “Computer-aided diagnosis of diabetic retinopathy: A review,” Comput. Biol. Med. 43(12), 2136–2155 (2013).
[Crossref] [PubMed]

M. M. R. Krishnan and O. Faust, “Automated glaucoma detection using hybrid feature extraction in retinal fundus images,” J. Mech. Med. Biol. 13(01), 1350011 (2013).
[Crossref]

2012 (1)

M. R. K. Mookiah, U. Rajendra Acharya, C. M. Lim, A. Petznick, and J. S. Suri, “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features,” Knowl.- Based Syst. 33, 73–82 (2012).
[Crossref]

2011 (1)

U. R. Acharya, S. Dua, X. Du, V. Sree S, and C. K. Chua, “Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features,” IEEE Trans. Inf. Technol. Biomed. 15(3), 449–455 (2011).
[Crossref] [PubMed]

2010 (6)

S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14(3), 471–481 (2010).
[Crossref] [PubMed]

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

E. A. Maul and H. D. Jampel, “Glaucoma Screening in the Real World,” Ophthalmology 117(9), 1665–1666 (2010).
[Crossref] [PubMed]

P. R. Healey, A. J. Lee, T. Aung, T. Y. Wong, and P. Mitchell, “Diagnostic Accuracy of the Heidelberg Retina Tomograph for Glaucoma A Population-Based Assessment,” Ophthalmology 117(9), 1667–1673 (2010).
[Crossref] [PubMed]

G. Li, A. K. Fansi, J.-F. Boivin, L. Joseph, and P. Harasymowycz, “Screening for Glaucoma in High-Risk Populations Using Optical Coherence Tomography,” Ophthalmology 117(3), 453–461 (2010).
[Crossref] [PubMed]

2008 (1)

A. L. Coleman and S. Miglior, “Risk Factors for Glaucoma Onset and Progression,” Surv. Ophthalmol. 53(6Suppl1), S3–S10 (2008).
[Crossref] [PubMed]

2007 (1)

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated Segmentation of the Optic Disc from Stereo Color Photographs Using Physiologically Plausible Features,” Invest. Ophthalmol. Vis. Sci. 48(4), 1665–1673 (2007).
[Crossref] [PubMed]

2006 (2)

T. R. Einarson, C. Vicente, M. Machado, D. Covert, G. E. Trope, and M. Iskedjian, “Screening for glaucoma in Canada: a systematic review of the literature,” Can. J. Ophthalmol. 41(6), 709–721 (2006).
[Crossref] [PubMed]

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
[Crossref]

2005 (2)

R. Fluss, D. Faraggi, and B. Reiser, “Estimation of the Youden Index and its Associated Cutoff Point,” Biom. J. 47(4), 458–472 (2005).
[Crossref] [PubMed]

C. Fleming, E. P. Whitlock, T. Beil, B. Smit, and R. P. Harris, “Screening for Primary Open-Angle Glaucoma in the Primary Care Setting: An Update for the US Preventive Services Task Force,” Ann. Fam. Med. 3(2), 167–170 (2005).
[Crossref] [PubMed]

2000 (1)

N. Yamada, P. P. Chen, R. P. Mills, M. M. Leen, R. L. Stamper, M. F. Lieberman, L. Xu, and D. C. Stanford, “Glaucoma screening using the scanning laser polarimeter,” J. Glaucoma 9(3), 254–261 (2000).
[Crossref] [PubMed]

1991 (1)

J. M. Tielsch, J. Katz, K. Singh, H. A. Quigley, J. D. Gottsch, J. Javitt, and A. Sommer, “A Population-based Evaluation of Glaucoma Screening: the Baltimore Eye Survey,” Am. J. Epidemiol. 134(10), 1102–1110 (1991).
[Crossref] [PubMed]

Abramoff, M. D.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Abràmoff, M. D.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated Segmentation of the Optic Disc from Stereo Color Photographs Using Physiologically Plausible Features,” Invest. Ophthalmol. Vis. Sci. 48(4), 1665–1673 (2007).
[Crossref] [PubMed]

Acharya, U. R.

S. Maheshwari, R. B. Pachori, and U. R. Acharya, “Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images,” IEEE J. Biomed. Health Inform. 21(3), 803–813 (2017).
[Crossref] [PubMed]

U. R. Acharya, S. Bhat, J. E. W. Koh, S. V. Bhandary, and H. Adeli, “A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images,” Comput. Biol. Med. 88, 72–83 (2017).
[Crossref] [PubMed]

M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, and A. Laude, “Computer-aided diagnosis of diabetic retinopathy: A review,” Comput. Biol. Med. 43(12), 2136–2155 (2013).
[Crossref] [PubMed]

U. R. Acharya, S. Dua, X. Du, V. Sree S, and C. K. Chua, “Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features,” IEEE Trans. Inf. Technol. Biomed. 15(3), 449–455 (2011).
[Crossref] [PubMed]

Adeli, H.

U. R. Acharya, S. Bhat, J. E. W. Koh, S. V. Bhandary, and H. Adeli, “A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images,” Comput. Biol. Med. 88, 72–83 (2017).
[Crossref] [PubMed]

Adni,

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
[Crossref] [PubMed]

Ahmady Phoulady, H.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Albarqouni, S.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Alward, W. L. M.

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated Segmentation of the Optic Disc from Stereo Color Photographs Using Physiologically Plausible Features,” Invest. Ophthalmol. Vis. Sci. 48(4), 1665–1673 (2007).
[Crossref] [PubMed]

Amelon, R.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Amgad, M.

P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. Velázquez Vega, D. J. Brat, and L. A. D. Cooper, “Predicting cancer outcomes from histology and genomics using convolutional networks,” Proc. Natl. Acad. Sci. U.S.A. 115(13), E2970–E2979 (2018).
[Crossref] [PubMed]

Annuscheit, J.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Antropova, N.

N. Antropova, B. Q. Huynh, and M. L. Giger, “A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets,” Med. Phys. 44(10), 5162–5171 (2017).
[Crossref] [PubMed]

Asaoka, R.

N. Shibata, M. Tanito, K. Mitsuhashi, Y. Fujino, M. Matsuura, H. Murata, and R. Asaoka, “Development of a deep residual learning algorithm to screen for glaucoma from fundus photography,” Sci. Rep. 8(1), 14665 (2018).
[Crossref] [PubMed]

Aung, T.

R. N. Weinreb, T. Aung, and F. A. Medeiros, “The Pathophysiology and Treatment of Glaucoma: A Review,” JAMA 311(18), 1901–1911 (2014).
[Crossref] [PubMed]

Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C.-Y. Cheng, “Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis,” Ophthalmology 121(11), 2081–2090 (2014).
[Crossref] [PubMed]

P. R. Healey, A. J. Lee, T. Aung, T. Y. Wong, and P. Mitchell, “Diagnostic Accuracy of the Heidelberg Retina Tomograph for Glaucoma A Population-Based Assessment,” Ophthalmology 117(9), 1667–1673 (2010).
[Crossref] [PubMed]

Awan, R.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Azuara-Blanco, A.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Balkenhol, M.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Barnholtz-Sloan, J. S.

P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. Velázquez Vega, D. J. Brat, and L. A. D. Cooper, “Predicting cancer outcomes from histology and genomics using convolutional networks,” Proc. Natl. Acad. Sci. U.S.A. 115(13), E2970–E2979 (2018).
[Crossref] [PubMed]

Beca, F.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Beck, A. H.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Beil, T.

C. Fleming, E. P. Whitlock, T. Beil, B. Smit, and R. P. Harris, “Screening for Primary Open-Angle Glaucoma in the Primary Care Setting: An Update for the US Preventive Services Task Force,” Ann. Fam. Med. 3(2), 167–170 (2005).
[Crossref] [PubMed]

Bejnordi, B. E.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Belghith, A.

M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
[Crossref] [PubMed]

Berg, A. C.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Bernstein, M.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Berseth, M.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Bhandary, S. V.

U. R. Acharya, S. Bhat, J. E. W. Koh, S. V. Bhandary, and H. Adeli, “A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images,” Comput. Biol. Med. 88, 72–83 (2017).
[Crossref] [PubMed]

Bhat, S.

U. R. Acharya, S. Bhat, J. E. W. Koh, S. V. Bhandary, and H. Adeli, “A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images,” Comput. Biol. Med. 88, 72–83 (2017).
[Crossref] [PubMed]

Blau, H. M.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Bock, R.

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14(3), 471–481 (2010).
[Crossref] [PubMed]

Boivin, J.-F.

G. Li, A. K. Fansi, J.-F. Boivin, L. Joseph, and P. Harasymowycz, “Screening for Glaucoma in High-Risk Populations Using Optical Coherence Tomography,” Ophthalmology 117(3), 453–461 (2010).
[Crossref] [PubMed]

Bowd, C.

M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
[Crossref] [PubMed]

Brat, D. J.

P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. Velázquez Vega, D. J. Brat, and L. A. D. Cooper, “Predicting cancer outcomes from histology and genomics using convolutional networks,” Proc. Natl. Acad. Sci. U.S.A. 115(13), E2970–E2979 (2018).
[Crossref] [PubMed]

Bressler, N. M.

P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
[Crossref] [PubMed]

Bruni, E.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Bueno, G.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Buhmann, J. M.

J. Zilly, J. M. Buhmann, and D. Mahapatra, “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation,” Comput. Med. Imaging Graph. 55, 28–41 (2017).
[Crossref] [PubMed]

Bult, P.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Burlina, P. M.

P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
[Crossref] [PubMed]

Burr, J. M.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Cai, W.

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
[Crossref] [PubMed]

Cairns, J.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Cao, X.

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation,” IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, and X. Cao, “Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image,” IEEE Trans. Med. Imaging 37(11), 2493–2501 (2018).
[Crossref] [PubMed]

Cazuguel, G.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Cetin-Atalay, R.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Chakraborty, D.

Chang, R. T.

Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs,” Ophthalmology 125(8), 1199–1206 (2018).
[Crossref] [PubMed]

Chatterjee, J.

Che, H.

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
[Crossref] [PubMed]

Chen, H.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Chen, P. P.

N. Yamada, P. P. Chen, R. P. Mills, M. M. Leen, R. L. Stamper, M. F. Lieberman, L. Xu, and D. C. Stanford, “Glaucoma screening using the scanning laser polarimeter,” J. Glaucoma 9(3), 254–261 (2000).
[Crossref] [PubMed]

Cheng, C.-Y.

Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C.-Y. Cheng, “Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis,” Ophthalmology 121(11), 2081–2090 (2014).
[Crossref] [PubMed]

Cheng, J.

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation,” IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, and X. Cao, “Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image,” IEEE Trans. Med. Imaging 37(11), 2493–2501 (2018).
[Crossref] [PubMed]

Christopher, M.

M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
[Crossref] [PubMed]

Chua, C. K.

M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, and A. Laude, “Computer-aided diagnosis of diabetic retinopathy: A review,” Comput. Biol. Med. 43(12), 2136–2155 (2013).
[Crossref] [PubMed]

U. R. Acharya, S. Dua, X. Du, V. Sree S, and C. K. Chua, “Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features,” IEEE Trans. Inf. Technol. Biomed. 15(3), 449–455 (2011).
[Crossref] [PubMed]

Ciompi, F.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Clarida, W.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Cochener, B.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Coleman, A. L.

A. L. Coleman and S. Miglior, “Risk Factors for Glaucoma Onset and Progression,” Surv. Ophthalmol. 53(6Suppl1), S3–S10 (2008).
[Crossref] [PubMed]

Cook, J.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Cooper, L. A. D.

P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. Velázquez Vega, D. J. Brat, and L. A. D. Cooper, “Predicting cancer outcomes from histology and genomics using convolutional networks,” Proc. Natl. Acad. Sci. U.S.A. 115(13), E2970–E2979 (2018).
[Crossref] [PubMed]

Coram, M.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Covert, D.

T. R. Einarson, C. Vicente, M. Machado, D. Covert, G. E. Trope, and M. Iskedjian, “Screening for glaucoma in Canada: a systematic review of the literature,” Can. J. Ophthalmol. 41(6), 709–721 (2006).
[Crossref] [PubMed]

Cree, M. J.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Crews, J.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Cuadros, J.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Cunefare, D.

Deeks, J.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Demirci, S.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Deng, J.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 248–255.
[Crossref]

Deniz, O.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Dong, W.

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 248–255.
[Crossref]

Dosto, N.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Dou, Q.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Du, X.

U. R. Acharya, S. Dua, X. Du, V. Sree S, and C. K. Chua, “Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features,” IEEE Trans. Inf. Technol. Biomed. 15(3), 449–455 (2011).
[Crossref] [PubMed]

Dua, S.

U. R. Acharya, S. Dua, X. Du, V. Sree S, and C. K. Chua, “Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features,” IEEE Trans. Inf. Technol. Biomed. 15(3), 449–455 (2011).
[Crossref] [PubMed]

Ehteshami Bejnordi, B.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Einarson, T. R.

T. R. Einarson, C. Vicente, M. Machado, D. Covert, G. E. Trope, and M. Iskedjian, “Screening for glaucoma in Canada: a systematic review of the literature,” Can. J. Ophthalmol. 41(6), 709–721 (2006).
[Crossref] [PubMed]

Erginay, A.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Esteva, A.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Fang, L.

Fansi, A. K.

G. Li, A. K. Fansi, J.-F. Boivin, L. Joseph, and P. Harasymowycz, “Screening for Glaucoma in High-Risk Populations Using Optical Coherence Tomography,” Ophthalmology 117(3), 453–461 (2010).
[Crossref] [PubMed]

Faraggi, D.

R. Fluss, D. Faraggi, and B. Reiser, “Estimation of the Youden Index and its Associated Cutoff Point,” Biom. J. 47(4), 458–472 (2005).
[Crossref] [PubMed]

Farsiu, S.

Faust, O.

M. M. R. Krishnan and O. Faust, “Automated glaucoma detection using hybrid feature extraction in retinal fundus images,” J. Mech. Med. Biol. 13(01), 1350011 (2013).
[Crossref]

Fawcett, T.

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
[Crossref]

Fei-Fei, L.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 248–255.
[Crossref]

Feng, D.

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
[Crossref] [PubMed]

Fernandez-Carrobles, M. M.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Fingert, J. H.

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated Segmentation of the Optic Disc from Stereo Color Photographs Using Physiologically Plausible Features,” Invest. Ophthalmol. Vis. Sci. 48(4), 1665–1673 (2007).
[Crossref] [PubMed]

Fleming, C.

C. Fleming, E. P. Whitlock, T. Beil, B. Smit, and R. P. Harris, “Screening for Primary Open-Angle Glaucoma in the Primary Care Setting: An Update for the US Preventive Services Task Force,” Ann. Fam. Med. 3(2), 167–170 (2005).
[Crossref] [PubMed]

Fluss, R.

R. Fluss, D. Faraggi, and B. Reiser, “Estimation of the Youden Index and its Associated Cutoff Point,” Biom. J. 47(4), 458–472 (2005).
[Crossref] [PubMed]

Folk, J. C.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Fraser, C.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Freund, D. E.

P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
[Crossref] [PubMed]

Friedman, D. S.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Fu, H.

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation,” IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, and X. Cao, “Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image,” IEEE Trans. Med. Imaging 37(11), 2493–2501 (2018).
[Crossref] [PubMed]

Fujino, Y.

N. Shibata, M. Tanito, K. Mitsuhashi, Y. Fujino, M. Matsuura, H. Murata, and R. Asaoka, “Development of a deep residual learning algorithm to screen for glaucoma from fundus photography,” Sci. Rep. 8(1), 14665 (2018).
[Crossref] [PubMed]

Fujita, H.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Fulham, M. J.

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
[Crossref] [PubMed]

Gajwani, P.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Gao, M.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Garcia, M.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Gargeya, R.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Geessink, O.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

George, A.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Ghafoorian, M.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Giger, M. L.

N. Antropova, B. Q. Huynh, and M. L. Giger, “A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets,” Med. Phys. 44(10), 5162–5171 (2017).
[Crossref] [PubMed]

B. Q. Huynh, H. Li, and M. L. Giger, “Digital mammographic tumor classification using transfer learning from deep convolutional neural networks,” J. Med. Imaging (Bellingham) 3(3), 034501 (2016).
[Crossref] [PubMed]

Girkin, C. A.

M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
[Crossref] [PubMed]

Goldbaum, M. H.

M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
[Crossref] [PubMed]

Gottsch, J. D.

J. M. Tielsch, J. Katz, K. Singh, H. A. Quigley, J. D. Gottsch, J. Javitt, and A. Sommer, “A Population-based Evaluation of Glaucoma Screening: the Baltimore Eye Survey,” Am. J. Epidemiol. 134(10), 1102–1110 (1991).
[Crossref] [PubMed]

Gotway, M. B.

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and Jianming Liang, “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016).
[Crossref] [PubMed]

Grant, A.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Greenlee, E. C.

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated Segmentation of the Optic Disc from Stereo Color Photographs Using Physiologically Plausible Features,” Invest. Ophthalmol. Vis. Sci. 48(4), 1665–1673 (2007).
[Crossref] [PubMed]

Guallar, E.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Gulshan, V.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Gurudu, S. R.

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and Jianming Liang, “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016).
[Crossref] [PubMed]

Gutman, D. A.

P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. Velázquez Vega, D. J. Brat, and L. A. D. Cooper, “Predicting cancer outcomes from histology and genomics using convolutional networks,” Proc. Natl. Acad. Sci. U.S.A. 115(13), E2970–E2979 (2018).
[Crossref] [PubMed]

Guymer, R. H.

Haleem, M. S.

M. S. Haleem, L. Han, J. van Hemert, and B. Li, “Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review,” Comput. Med. Imaging Graph. 37(7-8), 581–596 (2013).
[Crossref] [PubMed]

Halici, U.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Han, L.

M. S. Haleem, L. Han, J. van Hemert, and B. Li, “Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review,” Comput. Med. Imaging Graph. 37(7-8), 581–596 (2013).
[Crossref] [PubMed]

Harasymowycz, P.

G. Li, A. K. Fansi, J.-F. Boivin, L. Joseph, and P. Harasymowycz, “Screening for Glaucoma in High-Risk Populations Using Optical Coherence Tomography,” Ophthalmology 117(3), 453–461 (2010).
[Crossref] [PubMed]

Harris, R. P.

C. Fleming, E. P. Whitlock, T. Beil, B. Smit, and R. P. Harris, “Screening for Primary Open-Angle Glaucoma in the Primary Care Setting: An Update for the US Preventive Services Task Force,” Ann. Fam. Med. 3(2), 167–170 (2005).
[Crossref] [PubMed]

Haß, C.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Hatanaka, Y.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

He, M.

Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs,” Ophthalmology 125(8), 1199–1206 (2018).
[Crossref] [PubMed]

He, Y.

Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs,” Ophthalmology 125(8), 1199–1206 (2018).
[Crossref] [PubMed]

Healey, P. R.

P. R. Healey, A. J. Lee, T. Aung, T. Y. Wong, and P. Mitchell, “Diagnostic Accuracy of the Heidelberg Retina Tomograph for Glaucoma A Population-Based Assessment,” Ophthalmology 117(9), 1667–1673 (2010).
[Crossref] [PubMed]

Heng, P.-A.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Hermsen, M.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Hernández, R.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Hinton, G.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Hornegger, J.

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14(3), 471–481 (2010).
[Crossref] [PubMed]

Hornero, R.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Hoyng, C. B.

M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images,” IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016).
[Crossref] [PubMed]

Huang, Z.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Hufnagl, P.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Hurst, R. T.

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and Jianming Liang, “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016).
[Crossref] [PubMed]

Huynh, B. Q.

N. Antropova, B. Q. Huynh, and M. L. Giger, “A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets,” Med. Phys. 44(10), 5162–5171 (2017).
[Crossref] [PubMed]

B. Q. Huynh, H. Li, and M. L. Giger, “Digital mammographic tumor classification using transfer learning from deep convolutional neural networks,” J. Med. Imaging (Bellingham) 3(3), 034501 (2016).
[Crossref] [PubMed]

Irshad, H.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Iskedjian, M.

T. R. Einarson, C. Vicente, M. Machado, D. Covert, G. E. Trope, and M. Iskedjian, “Screening for glaucoma in Canada: a systematic review of the literature,” Can. J. Ophthalmol. 41(6), 709–721 (2006).
[Crossref] [PubMed]

Jampel, H. D.

E. A. Maul and H. D. Jampel, “Glaucoma Screening in the Real World,” Ophthalmology 117(9), 1665–1666 (2010).
[Crossref] [PubMed]

Javitt, J.

J. M. Tielsch, J. Katz, K. Singh, H. A. Quigley, J. D. Gottsch, J. Javitt, and A. Sommer, “A Population-based Evaluation of Glaucoma Screening: the Baltimore Eye Survey,” Am. J. Epidemiol. 134(10), 1102–1110 (1991).
[Crossref] [PubMed]

Jianming Liang,

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and Jianming Liang, “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016).
[Crossref] [PubMed]

Johannes van Diest, P.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Johnson, E.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Joseph, L.

G. Li, A. K. Fansi, J.-F. Boivin, L. Joseph, and P. Harasymowycz, “Screening for Glaucoma in High-Risk Populations Using Optical Coherence Tomography,” Ophthalmology 117(3), 453–461 (2010).
[Crossref] [PubMed]

Joshi, N.

P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
[Crossref] [PubMed]

Kalinovsky, A.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Kanwar, N.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Karpathy, A.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Karray, F.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Karri, S. P. K.

Karssemeijer, N.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Kartasalo, K.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Katz, J.

J. M. Tielsch, J. Katz, K. Singh, H. A. Quigley, J. D. Gottsch, J. Javitt, and A. Sommer, “A Population-based Evaluation of Glaucoma Screening: the Baltimore Eye Survey,” Am. J. Epidemiol. 134(10), 1102–1110 (1991).
[Crossref] [PubMed]

Keel, S.

Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs,” Ophthalmology 125(8), 1199–1206 (2018).
[Crossref] [PubMed]

Kendall, C. B.

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and Jianming Liang, “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016).
[Crossref] [PubMed]

Khosla, A.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Khvatkov, V.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Kikinis, R.

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
[Crossref] [PubMed]

Kim, C. Y.

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated Segmentation of the Optic Disc from Stereo Color Photographs Using Physiologically Plausible Features,” Invest. Ophthalmol. Vis. Sci. 48(4), 1665–1673 (2007).
[Crossref] [PubMed]

Kim, R.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Ko, J.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Koh, J. E. W.

U. R. Acharya, S. Bhat, J. E. W. Koh, S. V. Bhandary, and H. Adeli, “A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images,” Comput. Biol. Med. 88, 72–83 (2017).
[Crossref] [PubMed]

Kooi, T.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Kovalev, V.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Kraus, O.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Krause, J.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Krishnan, M. M. R.

M. M. R. Krishnan and O. Faust, “Automated glaucoma detection using hybrid feature extraction in retinal fundus images,” J. Mech. Med. Biol. 13(01), 1350011 (2013).
[Crossref]

Krizhevsky, A.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Kuprel, B.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Kwon, Y. H.

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated Segmentation of the Optic Disc from Stereo Color Photographs Using Physiologically Plausible Features,” Invest. Ophthalmol. Vis. Sci. 48(4), 1665–1673 (2007).
[Crossref] [PubMed]

Lamard, M.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Latonen, L.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Laude, A.

M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, and A. Laude, “Computer-aided diagnosis of diabetic retinopathy: A review,” Comput. Biol. Med. 43(12), 2136–2155 (2013).
[Crossref] [PubMed]

Lee, A. J.

P. R. Healey, A. J. Lee, T. Aung, T. Y. Wong, and P. Mitchell, “Diagnostic Accuracy of the Heidelberg Retina Tomograph for Glaucoma A Population-Based Assessment,” Ophthalmology 117(9), 1667–1673 (2010).
[Crossref] [PubMed]

Leen, M. M.

N. Yamada, P. P. Chen, R. P. Mills, M. M. Leen, R. L. Stamper, M. F. Lieberman, L. Xu, and D. C. Stanford, “Glaucoma screening using the scanning laser polarimeter,” J. Glaucoma 9(3), 254–261 (2000).
[Crossref] [PubMed]

Li, B.

M. S. Haleem, L. Han, J. van Hemert, and B. Li, “Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review,” Comput. Med. Imaging Graph. 37(7-8), 581–596 (2013).
[Crossref] [PubMed]

Li, G.

G. Li, A. K. Fansi, J.-F. Boivin, L. Joseph, and P. Harasymowycz, “Screening for Glaucoma in High-Risk Populations Using Optical Coherence Tomography,” Ophthalmology 117(3), 453–461 (2010).
[Crossref] [PubMed]

Li, H.

B. Q. Huynh, H. Li, and M. L. Giger, “Digital mammographic tumor classification using transfer learning from deep convolutional neural networks,” J. Med. Imaging (Bellingham) 3(3), 034501 (2016).
[Crossref] [PubMed]

Li, J.

S. Weng, X. Xu, J. Li, and S. T. C. Wong, “Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer,” J. Biomed. Opt. 22(10), 1–10 (2017).
[Crossref] [PubMed]

Li, K.

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 248–255.
[Crossref]

Li, L. J.

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 248–255.
[Crossref]

Li, Q.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Li, S.

Li, X.

Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C.-Y. Cheng, “Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis,” Ophthalmology 121(11), 2081–2090 (2014).
[Crossref] [PubMed]

Li, Z.

Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs,” Ophthalmology 125(8), 1199–1206 (2018).
[Crossref] [PubMed]

Liao, H.

H. Liao, “A Deep Learning Approach to Universal Skin Disease Classification,” 8 (2016).

Liauchuk, V.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Lieberman, M. F.

N. Yamada, P. P. Chen, R. P. Mills, M. M. Leen, R. L. Stamper, M. F. Lieberman, L. Xu, and D. C. Stanford, “Glaucoma screening using the scanning laser polarimeter,” J. Glaucoma 9(3), 254–261 (2000).
[Crossref] [PubMed]

Liebmann, J. M.

M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
[Crossref] [PubMed]

Liimatainen, K.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Lim, C. M.

M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, and A. Laude, “Computer-aided diagnosis of diabetic retinopathy: A review,” Comput. Biol. Med. 43(12), 2136–2155 (2013).
[Crossref] [PubMed]

M. R. K. Mookiah, U. Rajendra Acharya, C. M. Lim, A. Petznick, and J. S. Suri, “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features,” Knowl.- Based Syst. 33, 73–82 (2012).
[Crossref]

Lin, H.-J.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Litjens, G.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Liu, J.

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation,” IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, and X. Cao, “Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image,” IEEE Trans. Med. Imaging 37(11), 2493–2501 (2018).
[Crossref] [PubMed]

Liu, S.

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
[Crossref] [PubMed]

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
[Crossref] [PubMed]

Lou, Y.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Lourenco, T.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Lu, L.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Ma, S.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Machado, M.

T. R. Einarson, C. Vicente, M. Machado, D. Covert, G. E. Trope, and M. Iskedjian, “Screening for glaucoma in Canada: a systematic review of the literature,” Can. J. Ophthalmol. 41(6), 709–721 (2006).
[Crossref] [PubMed]

Madams, T.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Mahapatra, D.

J. Zilly, J. M. Buhmann, and D. Mahapatra, “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation,” Comput. Med. Imaging Graph. 55, 28–41 (2017).
[Crossref] [PubMed]

Maheshwari, S.

S. Maheshwari, R. B. Pachori, and U. R. Acharya, “Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images,” IEEE J. Biomed. Health Inform. 21(3), 803–813 (2017).
[Crossref] [PubMed]

Manson, Q. F.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Matsuda, H.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Matsuura, M.

N. Shibata, M. Tanito, K. Mitsuhashi, Y. Fujino, M. Matsuura, H. Murata, and R. Asaoka, “Development of a deep residual learning algorithm to screen for glaucoma from fundus photography,” Sci. Rep. 8(1), 14665 (2018).
[Crossref] [PubMed]

Maul, E. A.

E. A. Maul and H. D. Jampel, “Glaucoma Screening in the Real World,” Ophthalmology 117(9), 1665–1666 (2010).
[Crossref] [PubMed]

Mayo, A.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

McPherson, S.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Medeiros, F. A.

R. N. Weinreb, T. Aung, and F. A. Medeiros, “The Pathophysiology and Treatment of Glaucoma: A Review,” JAMA 311(18), 1901–1911 (2014).
[Crossref] [PubMed]

Mega, J. L.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Meier, J.

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14(3), 471–481 (2010).
[Crossref] [PubMed]

Meng, W.

Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs,” Ophthalmology 125(8), 1199–1206 (2018).
[Crossref] [PubMed]

Michelson, G.

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14(3), 471–481 (2010).
[Crossref] [PubMed]

Miglior, S.

A. L. Coleman and S. Miglior, “Risk Factors for Glaucoma Onset and Progression,” Surv. Ophthalmol. 53(6Suppl1), S3–S10 (2008).
[Crossref] [PubMed]

Mills, R. P.

N. Yamada, P. P. Chen, R. P. Mills, M. M. Leen, R. L. Stamper, M. F. Lieberman, L. Xu, and D. C. Stanford, “Glaucoma screening using the scanning laser polarimeter,” J. Glaucoma 9(3), 254–261 (2000).
[Crossref] [PubMed]

Mitchell, P.

P. R. Healey, A. J. Lee, T. Aung, T. Y. Wong, and P. Mitchell, “Diagnostic Accuracy of the Heidelberg Retina Tomograph for Glaucoma A Population-Based Assessment,” Ophthalmology 117(9), 1667–1673 (2010).
[Crossref] [PubMed]

Mitsuhashi, K.

N. Shibata, M. Tanito, K. Mitsuhashi, Y. Fujino, M. Matsuura, H. Murata, and R. Asaoka, “Development of a deep residual learning algorithm to screen for glaucoma from fundus photography,” Sci. Rep. 8(1), 14665 (2018).
[Crossref] [PubMed]

Mizutani, A.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Mobadersany, P.

P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. Velázquez Vega, D. J. Brat, and L. A. D. Cooper, “Predicting cancer outcomes from histology and genomics using convolutional networks,” Proc. Natl. Acad. Sci. U.S.A. 115(13), E2970–E2979 (2018).
[Crossref] [PubMed]

Mollura, D.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Mookiah, M. R. K.

M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, and A. Laude, “Computer-aided diagnosis of diabetic retinopathy: A review,” Comput. Biol. Med. 43(12), 2136–2155 (2013).
[Crossref] [PubMed]

M. R. K. Mookiah, U. Rajendra Acharya, C. M. Lim, A. Petznick, and J. S. Suri, “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features,” Knowl.- Based Syst. 33, 73–82 (2012).
[Crossref]

Mowatt, G.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Mudie, L.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Mungal, B.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Muramatsu, C.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Murata, H.

N. Shibata, M. Tanito, K. Mitsuhashi, Y. Fujino, M. Matsuura, H. Murata, and R. Asaoka, “Development of a deep residual learning algorithm to screen for glaucoma from fundus photography,” Sci. Rep. 8(1), 14665 (2018).
[Crossref] [PubMed]

Narayanaswamy, A.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Navab, N.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Nelson, P. C.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Ng, E. Y. K.

M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, and A. Laude, “Computer-aided diagnosis of diabetic retinopathy: A review,” Comput. Biol. Med. 43(12), 2136–2155 (2013).
[Crossref] [PubMed]

Niemeijer, M.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Nogues, I.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Novoa, R. A.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Nyúl, L. G.

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14(3), 471–481 (2010).
[Crossref] [PubMed]

Ogega, C.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Öner, M. Ü.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Pacheco, K. D.

P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
[Crossref] [PubMed]

Pachori, R. B.

S. Maheshwari, R. B. Pachori, and U. R. Acharya, “Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images,” IEEE J. Biomed. Health Inform. 21(3), 803–813 (2017).
[Crossref] [PubMed]

Pan, S. J.

S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

Pekala, M.

P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
[Crossref] [PubMed]

Peng, L.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Petznick, A.

M. R. K. Mookiah, U. Rajendra Acharya, C. M. Lim, A. Petznick, and J. S. Suri, “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features,” Knowl.- Based Syst. 33, 73–82 (2012).
[Crossref]

Proudfoot, J. A.

M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
[Crossref] [PubMed]

Pujol, S.

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
[Crossref] [PubMed]

Qaiser, T.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Quellec, G.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Quigley, H. A.

Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C.-Y. Cheng, “Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis,” Ophthalmology 121(11), 2081–2090 (2014).
[Crossref] [PubMed]

J. M. Tielsch, J. Katz, K. Singh, H. A. Quigley, J. D. Gottsch, J. Javitt, and A. Sommer, “A Population-based Evaluation of Glaucoma Screening: the Baltimore Eye Survey,” Am. J. Epidemiol. 134(10), 1102–1110 (1991).
[Crossref] [PubMed]

Rabindranath, K.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Racoceanu, D.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Rajendra Acharya, U.

M. R. K. Mookiah, U. Rajendra Acharya, C. M. Lim, A. Petznick, and J. S. Suri, “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features,” Knowl.- Based Syst. 33, 73–82 (2012).
[Crossref]

Rajpoot, N.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Raman, R.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Ramsay, C.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Reiser, B.

R. Fluss, D. Faraggi, and B. Reiser, “Estimation of the Youden Index and its Associated Cutoff Point,” Biom. J. 47(4), 458–472 (2005).
[Crossref] [PubMed]

Roth, H. R.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Roux, C.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Russakovsky, O.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Ruusuvuori, P.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Saaddine, J.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Salakhutdinov, R.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Sanchez, C. I.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Sánchez, C. I.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images,” IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016).
[Crossref] [PubMed]

Satheesh, S.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Seno, S.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Serrano, I.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Setio, A. A. A.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Sevastopolsky, A.

A. Sevastopolsky, “Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network,” Pattern Recognit. Image Anal. 27(3), 618–624 (2017).
[Crossref]

Shaban, M.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Shibata, N.

N. Shibata, M. Tanito, K. Mitsuhashi, Y. Fujino, M. Matsuura, H. Murata, and R. Asaoka, “Development of a deep residual learning algorithm to screen for glaucoma from fundus photography,” Sci. Rep. 8(1), 14665 (2018).
[Crossref] [PubMed]

Shin, H. C.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Shin, J. Y.

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and Jianming Liang, “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016).
[Crossref] [PubMed]

Shuba, L.

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated Segmentation of the Optic Disc from Stereo Color Photographs Using Physiologically Plausible Features,” Invest. Ophthalmol. Vis. Sci. 48(4), 1665–1673 (2007).
[Crossref] [PubMed]

Siddiqui, M. A.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Singh, K.

J. M. Tielsch, J. Katz, K. Singh, H. A. Quigley, J. D. Gottsch, J. Javitt, and A. Sommer, “A Population-based Evaluation of Glaucoma Screening: the Baltimore Eye Survey,” Am. J. Epidemiol. 134(10), 1102–1110 (1991).
[Crossref] [PubMed]

Sirinukunwattana, K.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Smit, B.

C. Fleming, E. P. Whitlock, T. Beil, B. Smit, and R. P. Harris, “Screening for Primary Open-Angle Glaucoma in the Primary Care Setting: An Update for the US Preventive Services Task Force,” Ann. Fam. Med. 3(2), 167–170 (2005).
[Crossref] [PubMed]

Socher, R.

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 248–255.
[Crossref]

Sommer, A.

J. M. Tielsch, J. Katz, K. Singh, H. A. Quigley, J. D. Gottsch, J. Javitt, and A. Sommer, “A Population-based Evaluation of Glaucoma Screening: the Baltimore Eye Survey,” Am. J. Epidemiol. 134(10), 1102–1110 (1991).
[Crossref] [PubMed]

Sosa-Ebert, A.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Sree S, V.

U. R. Acharya, S. Dua, X. Du, V. Sree S, and C. K. Chua, “Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features,” IEEE Trans. Inf. Technol. Biomed. 15(3), 449–455 (2011).
[Crossref] [PubMed]

Srivastava, N.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Stamper, R. L.

N. Yamada, P. P. Chen, R. P. Mills, M. M. Leen, R. L. Stamper, M. F. Lieberman, L. Xu, and D. C. Stanford, “Glaucoma screening using the scanning laser polarimeter,” J. Glaucoma 9(3), 254–261 (2000).
[Crossref] [PubMed]

Stanford, D. C.

N. Yamada, P. P. Chen, R. P. Mills, M. M. Leen, R. L. Stamper, M. F. Lieberman, L. Xu, and D. C. Stanford, “Glaucoma screening using the scanning laser polarimeter,” J. Glaucoma 9(3), 254–261 (2000).
[Crossref] [PubMed]

Stathonikos, N.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Stumpe, M. C.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Su, H.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Summers, R. M.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Suri, J. S.

M. R. K. Mookiah, U. Rajendra Acharya, C. M. Lim, A. Petznick, and J. S. Suri, “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features,” Knowl.- Based Syst. 33, 73–82 (2012).
[Crossref]

Sutskever, I.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Swenor, B.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Swetter, S. M.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Tajbakhsh, N.

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and Jianming Liang, “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016).
[Crossref] [PubMed]

Takenaka, Y.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Tanito, M.

N. Shibata, M. Tanito, K. Mitsuhashi, Y. Fujino, M. Matsuura, H. Murata, and R. Asaoka, “Development of a deep residual learning algorithm to screen for glaucoma from fundus photography,” Sci. Rep. 8(1), 14665 (2018).
[Crossref] [PubMed]

Tellez, D.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Tham, Y.-C.

Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C.-Y. Cheng, “Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis,” Ophthalmology 121(11), 2081–2090 (2014).
[Crossref] [PubMed]

Theelen, T.

M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images,” IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016).
[Crossref] [PubMed]

Thompson, S.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Thrun, S.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Tielsch, J. M.

J. M. Tielsch, J. Katz, K. Singh, H. A. Quigley, J. D. Gottsch, J. Javitt, and A. Sommer, “A Population-based Evaluation of Glaucoma Screening: the Baltimore Eye Survey,” Am. J. Epidemiol. 134(10), 1102–1110 (1991).
[Crossref] [PubMed]

Trope, G. E.

T. R. Einarson, C. Vicente, M. Machado, D. Covert, G. E. Trope, and M. Iskedjian, “Screening for glaucoma in Canada: a systematic review of the literature,” Can. J. Ophthalmol. 41(6), 709–721 (2006).
[Crossref] [PubMed]

Tsang, Y.-W.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Vale, L.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Valkonen, M.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

van der Laak, J. A. W. M.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

van Dijk, M. C.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

van Ginneken, B.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images,” IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016).
[Crossref] [PubMed]

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

van Grinsven, M. J. J. P.

M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images,” IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016).
[Crossref] [PubMed]

van Hemert, J.

M. S. Haleem, L. Han, J. van Hemert, and B. Li, “Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review,” Comput. Med. Imaging Graph. 37(7-8), 581–596 (2013).
[Crossref] [PubMed]

Varadaraj, V.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Velázquez Vega, J. E.

P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. Velázquez Vega, D. J. Brat, and L. A. D. Cooper, “Predicting cancer outcomes from histology and genomics using convolutional networks,” Proc. Natl. Acad. Sci. U.S.A. 115(13), E2970–E2979 (2018).
[Crossref] [PubMed]

Venâncio, R.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Venugopalan, S.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Veta, M.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Vicente, C.

T. R. Einarson, C. Vicente, M. Machado, D. Covert, G. E. Trope, and M. Iskedjian, “Screening for glaucoma in Canada: a systematic review of the literature,” Can. J. Ophthalmol. 41(6), 709–721 (2006).
[Crossref] [PubMed]

Vylegzhanin, A.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Wahl, M.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Wang, C.

Wang, D.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Watanabe, S.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Webster, D. R.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Weinreb, R. N.

M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
[Crossref] [PubMed]

R. N. Weinreb, T. Aung, and F. A. Medeiros, “The Pathophysiology and Treatment of Glaucoma: A Review,” JAMA 311(18), 1901–1911 (2014).
[Crossref] [PubMed]

Weng, S.

S. Weng, X. Xu, J. Li, and S. T. C. Wong, “Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer,” J. Biomed. Opt. 22(10), 1–10 (2017).
[Crossref] [PubMed]

Whitlock, E. P.

C. Fleming, E. P. Whitlock, T. Beil, B. Smit, and R. P. Harris, “Screening for Primary Open-Angle Glaucoma in the Primary Care Setting: An Update for the US Preventive Services Task Force,” Ann. Fam. Med. 3(2), 167–170 (2005).
[Crossref] [PubMed]

Widner, K.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Wong, D. W. K.

H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, and X. Cao, “Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image,” IEEE Trans. Med. Imaging 37(11), 2493–2501 (2018).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation,” IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018).
[Crossref] [PubMed]

Wong, Q.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Wong, S. T. C.

S. Weng, X. Xu, J. Li, and S. T. C. Wong, “Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer,” J. Biomed. Opt. 22(10), 1–10 (2017).
[Crossref] [PubMed]

Wong, T. Y.

Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C.-Y. Cheng, “Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis,” Ophthalmology 121(11), 2081–2090 (2014).
[Crossref] [PubMed]

P. R. Healey, A. J. Lee, T. Aung, T. Y. Wong, and P. Mitchell, “Diagnostic Accuracy of the Heidelberg Retina Tomograph for Glaucoma A Population-Based Assessment,” Ophthalmology 117(9), 1667–1673 (2010).
[Crossref] [PubMed]

Wormald, R.

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

Wu, D.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Wu, X.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Xu, L.

N. Yamada, P. P. Chen, R. P. Mills, M. M. Leen, R. L. Stamper, M. F. Lieberman, L. Xu, and D. C. Stanford, “Glaucoma screening using the scanning laser polarimeter,” J. Glaucoma 9(3), 254–261 (2000).
[Crossref] [PubMed]

Xu, X.

S. Weng, X. Xu, J. Li, and S. T. C. Wong, “Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer,” J. Biomed. Opt. 22(10), 1–10 (2017).
[Crossref] [PubMed]

Xu, Y.

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation,” IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, and X. Cao, “Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image,” IEEE Trans. Med. Imaging 37(11), 2493–2501 (2018).
[Crossref] [PubMed]

Xu, Z.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

Yamada, N.

N. Yamada, P. P. Chen, R. P. Mills, M. M. Leen, R. L. Stamper, M. F. Lieberman, L. Xu, and D. C. Stanford, “Glaucoma screening using the scanning laser polarimeter,” J. Glaucoma 9(3), 254–261 (2000).
[Crossref] [PubMed]

Yang, Q.

S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

Yao, J.

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

You, J.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Yousefi, S.

P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. Velázquez Vega, D. J. Brat, and L. A. D. Cooper, “Predicting cancer outcomes from histology and genomics using convolutional networks,” Proc. Natl. Acad. Sci. U.S.A. 115(13), E2970–E2979 (2018).
[Crossref] [PubMed]

Zangwill, L. M.

M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
[Crossref] [PubMed]

Zhang, B.

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

Zhang, C.

H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, and X. Cao, “Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image,” IEEE Trans. Med. Imaging 37(11), 2493–2501 (2018).
[Crossref] [PubMed]

Zhao, D.

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Zhong, A.

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

Zilly, J.

J. Zilly, J. M. Buhmann, and D. Mahapatra, “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation,” Comput. Med. Imaging Graph. 55, 28–41 (2017).
[Crossref] [PubMed]

Am. J. Epidemiol. (1)

J. M. Tielsch, J. Katz, K. Singh, H. A. Quigley, J. D. Gottsch, J. Javitt, and A. Sommer, “A Population-based Evaluation of Glaucoma Screening: the Baltimore Eye Survey,” Am. J. Epidemiol. 134(10), 1102–1110 (1991).
[Crossref] [PubMed]

Am. J. Ophthalmol. (1)

D. Zhao, E. Guallar, P. Gajwani, B. Swenor, J. Crews, J. Saaddine, L. Mudie, V. Varadaraj, D. S. Friedman, N. Kanwar, A. Sosa-Ebert, N. Dosto, S. Thompson, M. Wahl, E. Johnson, and C. Ogega, “Optimizing Glaucoma Screening in High-Risk Population: Design and 1-Year Findings of the Screening to Prevent (SToP) Glaucoma Study,” Am. J. Ophthalmol. 180, 18–28 (2017).
[Crossref] [PubMed]

Ann. Fam. Med. (1)

C. Fleming, E. P. Whitlock, T. Beil, B. Smit, and R. P. Harris, “Screening for Primary Open-Angle Glaucoma in the Primary Care Setting: An Update for the US Preventive Services Task Force,” Ann. Fam. Med. 3(2), 167–170 (2005).
[Crossref] [PubMed]

Biom. J. (1)

R. Fluss, D. Faraggi, and B. Reiser, “Estimation of the Youden Index and its Associated Cutoff Point,” Biom. J. 47(4), 458–472 (2005).
[Crossref] [PubMed]

Biomed. Opt. Express (2)

Can. J. Ophthalmol. (1)

T. R. Einarson, C. Vicente, M. Machado, D. Covert, G. E. Trope, and M. Iskedjian, “Screening for glaucoma in Canada: a systematic review of the literature,” Can. J. Ophthalmol. 41(6), 709–721 (2006).
[Crossref] [PubMed]

Comput. Biol. Med. (2)

M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, and A. Laude, “Computer-aided diagnosis of diabetic retinopathy: A review,” Comput. Biol. Med. 43(12), 2136–2155 (2013).
[Crossref] [PubMed]

U. R. Acharya, S. Bhat, J. E. W. Koh, S. V. Bhandary, and H. Adeli, “A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images,” Comput. Biol. Med. 88, 72–83 (2017).
[Crossref] [PubMed]

Comput. Med. Imaging Graph. (2)

J. Zilly, J. M. Buhmann, and D. Mahapatra, “Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation,” Comput. Med. Imaging Graph. 55, 28–41 (2017).
[Crossref] [PubMed]

M. S. Haleem, L. Han, J. van Hemert, and B. Li, “Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review,” Comput. Med. Imaging Graph. 37(7-8), 581–596 (2013).
[Crossref] [PubMed]

IEEE J. Biomed. Health Inform. (1)

S. Maheshwari, R. B. Pachori, and U. R. Acharya, “Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images,” IEEE J. Biomed. Health Inform. 21(3), 803–813 (2017).
[Crossref] [PubMed]

IEEE Trans. Biomed. Eng. (1)

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol, R. Kikinis, D. Feng, M. J. Fulham, Adni, and ADNI, “Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease,” IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015).
[Crossref] [PubMed]

IEEE Trans. Inf. Technol. Biomed. (1)

U. R. Acharya, S. Dua, X. Du, V. Sree S, and C. K. Chua, “Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features,” IEEE Trans. Inf. Technol. Biomed. 15(3), 449–455 (2011).
[Crossref] [PubMed]

IEEE Trans. Knowl. Data Eng. (1)

S. J. Pan and Q. Yang, “A Survey on Transfer Learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

IEEE Trans. Med. Imaging (6)

N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and Jianming Liang, “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016).
[Crossref] [PubMed]

M. Niemeijer, B. van Ginneken, M. J. Cree, A. Mizutani, G. Quellec, C. I. Sanchez, B. Zhang, R. Hornero, M. Lamard, C. Muramatsu, X. Wu, G. Cazuguel, J. You, A. Mayo, Q. Li, Y. Hatanaka, B. Cochener, C. Roux, F. Karray, M. Garcia, H. Fujita, and M. D. Abramoff, “Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs,” IEEE Trans. Med. Imaging 29(1), 185–195 (2010).
[Crossref] [PubMed]

H. C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation,” IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018).
[Crossref] [PubMed]

H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, and X. Cao, “Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image,” IEEE Trans. Med. Imaging 37(11), 2493–2501 (2018).
[Crossref] [PubMed]

M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images,” IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016).
[Crossref] [PubMed]

Int. J. Comput. Vis. (1)

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Invest. Ophthalmol. Vis. Sci. (2)

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

M. D. Abràmoff, W. L. M. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, and Y. H. Kwon, “Automated Segmentation of the Optic Disc from Stereo Color Photographs Using Physiologically Plausible Features,” Invest. Ophthalmol. Vis. Sci. 48(4), 1665–1673 (2007).
[Crossref] [PubMed]

J. Biomed. Opt. (1)

S. Weng, X. Xu, J. Li, and S. T. C. Wong, “Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer,” J. Biomed. Opt. 22(10), 1–10 (2017).
[Crossref] [PubMed]

J. Glaucoma (1)

N. Yamada, P. P. Chen, R. P. Mills, M. M. Leen, R. L. Stamper, M. F. Lieberman, L. Xu, and D. C. Stanford, “Glaucoma screening using the scanning laser polarimeter,” J. Glaucoma 9(3), 254–261 (2000).
[Crossref] [PubMed]

J. Mach. Learn. Res. (1)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

J. Mech. Med. Biol. (1)

M. M. R. Krishnan and O. Faust, “Automated glaucoma detection using hybrid feature extraction in retinal fundus images,” J. Mech. Med. Biol. 13(01), 1350011 (2013).
[Crossref]

J. Med. Imaging (Bellingham) (1)

B. Q. Huynh, H. Li, and M. L. Giger, “Digital mammographic tumor classification using transfer learning from deep convolutional neural networks,” J. Med. Imaging (Bellingham) 3(3), 034501 (2016).
[Crossref] [PubMed]

JAMA (3)

R. N. Weinreb, T. Aung, and F. A. Medeiros, “The Pathophysiology and Treatment of Glaucoma: A Review,” JAMA 311(18), 1901–1911 (2014).
[Crossref] [PubMed]

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

B. Ehteshami Bejnordi, M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H.-J. Lin, P.-A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y.-W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio, “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,” JAMA 318(22), 2199–2210 (2017).
[Crossref] [PubMed]

JAMA Ophthalmol. (1)

P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
[Crossref] [PubMed]

Knowl.- Based Syst. (1)

M. R. K. Mookiah, U. Rajendra Acharya, C. M. Lim, A. Petznick, and J. S. Suri, “Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features,” Knowl.- Based Syst. 33, 73–82 (2012).
[Crossref]

Med. Image Anal. (2)

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal. 14(3), 471–481 (2010).
[Crossref] [PubMed]

Med. Phys. (1)

N. Antropova, B. Q. Huynh, and M. L. Giger, “A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets,” Med. Phys. 44(10), 5162–5171 (2017).
[Crossref] [PubMed]

Nature (1)

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Ophthalmology (5)

Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs,” Ophthalmology 125(8), 1199–1206 (2018).
[Crossref] [PubMed]

P. R. Healey, A. J. Lee, T. Aung, T. Y. Wong, and P. Mitchell, “Diagnostic Accuracy of the Heidelberg Retina Tomograph for Glaucoma A Population-Based Assessment,” Ophthalmology 117(9), 1667–1673 (2010).
[Crossref] [PubMed]

G. Li, A. K. Fansi, J.-F. Boivin, L. Joseph, and P. Harasymowycz, “Screening for Glaucoma in High-Risk Populations Using Optical Coherence Tomography,” Ophthalmology 117(3), 453–461 (2010).
[Crossref] [PubMed]

Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C.-Y. Cheng, “Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis,” Ophthalmology 121(11), 2081–2090 (2014).
[Crossref] [PubMed]

E. A. Maul and H. D. Jampel, “Glaucoma Screening in the Real World,” Ophthalmology 117(9), 1665–1666 (2010).
[Crossref] [PubMed]

Pattern Recognit. Image Anal. (1)

A. Sevastopolsky, “Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network,” Pattern Recognit. Image Anal. 27(3), 618–624 (2017).
[Crossref]

Pattern Recognit. Lett. (1)

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27(8), 861–874 (2006).
[Crossref]

Proc. Natl. Acad. Sci. U.S.A. (1)

P. Mobadersany, S. Yousefi, M. Amgad, D. A. Gutman, J. S. Barnholtz-Sloan, J. E. Velázquez Vega, D. J. Brat, and L. A. D. Cooper, “Predicting cancer outcomes from histology and genomics using convolutional networks,” Proc. Natl. Acad. Sci. U.S.A. 115(13), E2970–E2979 (2018).
[Crossref] [PubMed]

Sci. Rep. (2)

N. Shibata, M. Tanito, K. Mitsuhashi, Y. Fujino, M. Matsuura, H. Murata, and R. Asaoka, “Development of a deep residual learning algorithm to screen for glaucoma from fundus photography,” Sci. Rep. 8(1), 14665 (2018).
[Crossref] [PubMed]

M. Christopher, A. Belghith, C. Bowd, J. A. Proudfoot, M. H. Goldbaum, R. N. Weinreb, C. A. Girkin, J. M. Liebmann, and L. M. Zangwill, “Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs,” Sci. Rep. 8(1), 16685 (2018).
[Crossref] [PubMed]

Surv. Ophthalmol. (1)

A. L. Coleman and S. Miglior, “Risk Factors for Glaucoma Onset and Progression,” Surv. Ophthalmol. 53(6Suppl1), S3–S10 (2008).
[Crossref] [PubMed]

Other (23)

M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” in Computer Vision – ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, eds., Lecture Notes in Computer Science (Springer International Publishing, 2014), pp. 818–833.

T. Xu, H. Zhang, X. Huang, S. Zhang, and D. N. Metaxas, “Multimodal Deep Learning for Cervical Dysplasia Diagnosis,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Lecture Notes in Computer Science (Springer, Cham, 2016), pp. 115–123.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” ArXiv151203385 Cs (2015).

Sander Dieleman, Jan Schlüter, Colin Raffel, Eben Olson, Søren Kaae Sønderby, Daniel Nouri, Daniel Maturana, Martin Thoma, Eric Battenberg, Jack Kelly, Jeffrey De Fauw, Michael Heilman, diogo149, Brian McFee, Hendrik Weideman, takacsg84, peterderivaz, Jon, instagibbs, Dr. Kashif Rasul, CongLiu, Britefury, and Jonas Degrave, Lasagne: First Release. (Zenodo, 2015).

F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio, “Theano: new features and speed improvements,” ArXiv12115590 Cs (2012).

H. Liao, “A Deep Learning Approach to Universal Skin Disease Classification,” 8 (2016).

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going Deeper With Convolutions,” in (2015), pp. 1–9.

F. Fumero, S. Alayon, J. L. Sanchez, J. Sigut, and M. Gonzalez-Hernandez, “RIM-ONE: An open retinal image database for optic nerve evaluation,” in 2011 24th International Symposium on Computer-Based Medical Systems (CBMS) (2011), pp. 1–6.
[Crossref]

J. Sivaswamy, S. R. Krishnadas, G. D. Joshi, M. Jain, and A. U. S. Tabish, “Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation,” in 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) (2014), pp. 53–56.
[Crossref]

S. Sekhar, W. Al-Nuaimy, and A. K. Nandi, “Automated localisation of optic disk and fovea in retinal fundus images,” in 2008 16th European Signal Processing Conference (2008), pp. 1–5.

S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” ArXiv150203167 Cs (2015).

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” ArXiv14091556 Cs (2014).

H. Muhammad, T. J. Fuchs, N. De Cuir, C. G. De Moraes, D. M. Blumberg, J. M. Liebmann, R. Ritch, and D. C. Hood, “Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects,” https://www.ingentaconnect.com/content/wk/jglau/2017/00000026/00000012/art00008 .
[Crossref]

K. Gopinath, S. B. Rangrej, and J. Sivaswamy, “A deep learning framework for segmentation of retinal layers from OCT images,” ArXiv180608859 Cs (2018).

X. Chen, Y. Xu, D. W. K. Wong, T. Y. Wong, and J. Liu, “Glaucoma detection based on deep convolutional neural network,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2015), pp. 715–718.
[Crossref]

B. Al-Bander, W. Al-Nuaimy, M. A. Al-Taee, and Y. Zheng, “Automated glaucoma diagnosis using deep learning approach,” in 2017 14th International Multi-Conference on Systems, Signals Devices (SSD) (2017), pp. 207–210.

H. Fu, Y. Xu, S. Lin, D. W. K. Wong, and J. Liu, “DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Lecture Notes in Computer Science (Springer, Cham, 2016), pp. 132–139.

D. Mahapatra, P. K. Roy, S. Sedai, and R. Garnavi, “Retinal Image Quality Classification Using Saliency Maps and CNNs,” in Machine Learning in Medical Imaging, Lecture Notes in Computer Science (Springer, Cham, 2016), pp. 172–179.

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 248–255.
[Crossref]

K. Fukushima and S. Miyake, “Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition,” in Competition and Cooperation in Neural Nets, Lecture Notes in Biomathematics (Springer, Berlin, Heidelberg, 1982), pp. 267–285.

A. M. Ervin, M. V. Boland, E. H. Myrowitz, J. Prince, B. Hawkins, D. Vollenweider, D. Ward, C. Suarez-Cuervo, and K. A. Robinson, “Screening for Glaucoma: Comparative Effectiveness,” D): Agency for Healthcare Research and Quality (US); 2012 Apr. Report No.: 12-EHC037-EF (2012).

J. M. Burr, G. Mowatt, R. Hernández, M. A. Siddiqui, J. Cook, T. Lourenco, C. Ramsay, L. Vale, C. Fraser, A. Azuara-Blanco, J. Deeks, J. Cairns, R. Wormald, S. McPherson, K. Rabindranath, and A. Grant, “The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation,” Health Technol. Assess.11(41), 1–190 (2007).
[Crossref] [PubMed]

T. Walter and J.-C. Klein, “Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques,” in Medical Data Analysis, Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2001), pp. 282–287.

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (11)

Fig. 1
Fig. 1 Example of findings used to detect glaucoma in color fundus images. (a) Quantification of the optic cup to disc ratio (CDR). The reduction of the optic nerve fibres (typically related with glaucoma) provokes optic disc cupping, central cup becomes larger, with respect to the optic disc (b) The neuroretinal rim usually follows a normal pattern (ISNT rule) where the inferior region is broader than the superior, broader than the nasal, and broader than the temporal region. The alteration of this pattern is a suspicious sign of glaucoma
Fig. 2
Fig. 2 Examples of color fundus images from the ESPERANZA data set. All images were labeled with good quality by the evaluators. (a1-2) Left eye of a glaucoma suspect disc with unaccomplished ISNT rule (inferior rim is not wider than superior rim). (b1-2) Right eye of a glaucoma suspect disc due to superior and temporal rim thinning. (c1-2) Left eye with of a normal disc.
Fig. 3
Fig. 3 Architecture of the standard CNN.
Fig. 4
Fig. 4 Global performance comparison ROC curves and AUC values of the comparison of the networks of the study using the test set described in Table 5. The first option in terms of AUC is labeled as (1).
Fig. 5
Fig. 5 ROC curves and AUC values for the 10-fold cross validation experiment on VGG19 TL.
Fig. 6
Fig. 6 ROC curve of VGG19 with fine tuning (VGG19_TL) over the test set of Table 5 and relevant operating points. (a) Expert specificity operating point of the ROC curve with sensitivity 85.48% and specificity 89.67% (b) Expert sensitivity operating point of the ROC curve with sensitivity 77.41% and specificity 93.18% (c) Reference performance of an ophthalmologist expert in glaucoma considering all the ESPERANZA data set, sensitivity 76.62% and specificity 89.14%. (d) Reference performance of an ophthalmologist non expert in glaucoma considering all the ESPERANZA data set, sensitivity 58.75% and specificity 86.07%.
Fig. 7
Fig. 7 Confusion matrixes for the test data set considering a training of VGG19 with data augmentation on the fly. (a) All the test data set of the study. (b) Test set from the ESPERANZA database. (c) Test set from DRISHTI-GS database. (d) Test set from RIM-ONE database.
Fig. 8
Fig. 8 Example of the classification of the VGG19_TL network for images from the test set of the ESPERANZA database. (a, b) True negative examples, the human evaluators and the algorithm identified the images as normal. (c, d) True positives examples, the human evaluators and the algorithm identified the images as glaucoma. (e, f) False negatives examples. The human evaluation identified both images as glaucoma but the algorithm labeled them as normal. (g, h) False positives examples. The human evaluators marked the images as normal but the algorithm classified them as glaucoma.
Fig. 9
Fig. 9 ROC curves and AUC values using always the test set of Table 5 and training VGG19 with fine tuning and 100 epochs. The red line represents the ROC curve using the network trained only with the EPSERANZA data set. The blue line is the result after training with ESPERANZA and DRISHTI-GS data sets and the green line is the ROC curve after training using all the data sets of the study: ESPERANZA, DRISHTI-GS and RIM-ONE.
Fig. 10
Fig. 10 CNN Model based on VGG19 integrating the clinical data and color fundus image. The clinical data were incorporated to the model in the last fully connected layer. In the first experiment we included 8 clinical data and in the second the two selected from the analysis of Table 11.
Fig. 11
Fig. 11 ROC curves and confusion matrices of the integration of images and clinical data. (a) ROC curves of the three trainings using only the ESPERANZA data set (b) Confusion matrix considering only the color fundus images (reference). (c) Confusion matrix considering the images with the age and personal record of glaucoma. (d) Confusion matrix considering the images with all clinical data collected.

Tables (12)

Tables Icon

Table 1 Summary of methods for the detection of glaucoma in color fundus images. In the data sets column we indicate the number of normal cases (-) and glaucoma cases ( + ). For the performance we used the reported metric used in the study: AUC, accuracy (Acc) and specificity (Sp) and sensitivity (Sn).

Tables Icon

Table 2 Data sets used in the study. The term “glaucoma” includes all retinal images in the data sets classified by a specialist as suspect of glaucoma or as suffering the disease in any stage (early, moderate or severe glaucoma).

Tables Icon

Table 3 Specificity and sensitivity (defined in section 2.4) reference in the classification of the experts and non-experts evaluators respect to the gold standard in the ESPERANZA data set. The values were calculated considering all the images evaluated by the ophthalmologists during the campaign with quality good or enough.

Tables Icon

Table 4 Detail of the layers and parameters defined in the standard CNN. The input of the network is an image of size 256x256x3 and the output are two scores of the two classes considered. In the parameters column K indicates the number of filters of the layer. After each convolution layer, batch normalization was applied.

Tables Icon

Table 5 Distribution of images in the groups “glaucoma” and “normal”.

Tables Icon

Table 6 Number of epochs selected after the monitoring of the training process using the validation set.

Tables Icon

Table 7 Performance ratios of all the CNNs evaluated. The sensitivity and specificity were calculated using the Youden index previously described. The networks were evaluated with the total test set (370 normal and 124 glaucoma). The best option for each metric is highlighted in bold.

Tables Icon

Table 8 Performance ratios of all the CNNs evaluated considering a subset of the test set including the images from ESPERANZA and DRIHSTI-GS data sets set (343 normal and 45 glaucoma). The sensitivity and specificity were calculated using the Youden index previously described. The best option in each metric is highlighted in bold.

Tables Icon

Table 9 Performance ratios of the Ensemble of the full training and fine tuned of each CNN.

Tables Icon

Table 10 Mean IOP and standard deviation and mean age and standard deviation in the training set and test set in the glaucoma and normal classification.

Tables Icon

Table 11 Percentage of cases with glaucoma family history, personal record of glaucoma and personal related therapy in the training and test set of the glaucoma and normal classification.

Tables Icon

Table 12 AUC, specificity and sensitivity values corresponding with the result of applying the network trained with the ESPERANZA data set considering only the color fundus images (reference), integrating the images with all the data collected from the examination of the patient (fusion all data) and integrating only the age and personal record of glaucoma (fusion age/personal record glaucoma).

Metrics