Abstract

Optical coherence tomography (OCT) is a promising high-speed, non-invasive imaging modality providing high-resolution retinal scans. However, a variety of external factors such as light occlusion and patient movement can seriously degrade OCT image quality, which complicates manual retinopathy detection and computer-aided diagnosis. As such, this study first presents an OCT image quality assessment (OCT-IQA) system, capable of automatic classification based on signal completeness, location, and effectiveness. Four CNN architectures (VGG-16, Inception-V3, ResNet-18, and ResNet-50) from the ImageNet classification task were used to train the proposed OCT-IQA system via transfer learning. The ResNet-50 with the best performance was then integrated into the final OCT-IQA network. The usefulness of this approach was evaluated using retinopathy detection results. A retinopathy classification network was first trained by fine-tuning Inception-V3 model. The model was then applied to two test datasets, created randomly from the original dataset, one of which was screened by the OCT-IQA system and only included high quality images while the other was mixed by high and low quality images. Results showed that retinopathy detection accuracy and area under curve (AUC) were 3.75% and 1.56% higher, respectively, for the filtered data (compared with the unfiltered data). These experimental results demonstrate the effectiveness of the proposed OCT-IQA system and suggest that deep learning could be applied to the design of computer-aided systems (CADSs) for automatic retinopathy detection.

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

Full Article  |  PDF Article
OSA Recommended Articles
Deep learning-based automated detection of retinal diseases using optical coherence tomography images

Feng Li, Hua Chen, Zheng Liu, Xue-dian Zhang, Min-shan Jiang, Zhi-zheng Wu, and Kai-qian Zhou
Biomed. Opt. Express 10(12) 6204-6226 (2019)

Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

Juan J. Gómez-Valverde, Alfonso Antón, Gianluca Fatti, Bart Liefers, Alejandra Herranz, Andrés Santos, Clara I. Sánchez, and María J. Ledesma-Carbayo
Biomed. Opt. Express 10(2) 892-913 (2019)

Characterization of coronary artery pathological formations from OCT imaging using deep learning

Atefeh Abdolmanafi, Luc Duong, Nagib Dahdah, Ibrahim Ragui Adib, and Farida Cheriet
Biomed. Opt. Express 9(10) 4936-4960 (2018)

References

  • View by:
  • |
  • |
  • |

  1. M. E. Velthoven, D. J. Faber, F. D. Verbraak, T. G. van Leeuwen, and M. D. de Smet, “Recent developments in optical coherence tomography for imaging the retina,” Prog. Retinal Eye Res. 26(1), 57–77 (2007).
    [Crossref]
  2. A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
    [Crossref]
  3. E. A. Swanson and J. G. Fujimoto, “The ecosystem that powered the translation of OCT from fundamental research to clinical and commercial impact,” Biomed. Opt. Express 8(3), 1638–1664 (2017).
    [Crossref]
  4. 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]
  5. S. Farsiu, S. J. Chiu, R. V. O’ Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
    [Crossref]
  6. Y. Wang, Y. Zhang, Z. Yao, R. Zhao, and F. Zhou, “Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images,” Biomed. Opt. Express 7(12), 4928–4940 (2016).
    [Crossref]
  7. R. Rasti, H. Rabbani, A. Mehridehnavi, and F. Hajizadeh, “Macular OCT classification using a multi-scale convolutional neural network ensemble,” IEEE Trans. Med. Imaging 37(4), 1024–1034 (2018).
    [Crossref]
  8. M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefe’s Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
    [Crossref]
  9. D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
    [Crossref]
  10. H. Ishikawa, G. Wollstein, M. Aoyama, D. Stein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Stratus OCT image quality assessment,” Invest. Ophthalmol. Visual Sci. 45(13), 3317 (2004).
  11. D. M. Stein, H. Ishikawa, R. Hariprasad, G. Wollstein, R. J. Noecker, J. G. Fujimoto, and J. S. Schuman, “A new quality assessment parameter for optical coherence tomography,” Br. J. Ophthalmol. 90(2), 186–190 (2006).
    [Crossref]
  12. S. Liu, A. S. Paranjape, B. Elmaanaoui, J. Dewelle, H. G. Rylander, M. K. Markey, and T. E. Milner, “Quality assessment for spectral domain optical coherence tomography (OCT) images,” In Multimodal Biomedical Imaging IV (Vol. 7171, p. 71710X). International Society for Optics and Photonics (2009).
  13. Y. Huang, S. Gangaputra, K. E. Lee, A. R. Narkar, R. Klein, B. E. K. Klein, S. M. Meuer, and R. P. Danis, “Signal Quality Assessment of Retinal Optical Coherence Tomography Images. Signal quality assessment of retinal optical coherence tomography images,” Invest. Ophthalmol. Visual Sci. 53(4), 2133–2141 (2012).
    [Crossref]
  14. X. Gao, F. Gao, D. Tao, and X. Li, “Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning,” IEEE Trans. Neural Netw. Learning Syst. 24(12), 2013–2026 (2013).
    [Crossref]
  15. K. Gu, G. Zhai, X. Yang, and W. Zhang, “Deep learning network for blind image quality assessment,” In 2014 IEEE International Conference on Image Processing (ICIP), (2014, October), pp. 511–515. IEEE.
  16. R. Tennakoon, D. Mahapatra, P. Roy, S. Sedai, and R. Garnavi, “Image Quality Classification for DR Screening Using Convolutional Neural Networks,” In: X. Chen, M. K. Garvin, J. Liu, E. Trucco, and Y. Xu, eds. Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, OMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, (October 21, 2016), 113–120.
  17. S. K. Saha, B. Fernando, J. Cuadros, D. Xiao, and Y. Kanagasingam, “Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening,” arXiv preprint arXiv:1703.02511. (2017).
  18. J. Sun, C. Wan, J. Cheng, F. Yu, and J. Liu, “Retinal image quality classification using fine-tuned CNN,” In Fetal, Infant and Ophthalmic Medical Image Analysis, (2017), pp. 126–133. Springer, Cham.
  19. F. Yu, J. Sun, A. Li, J. Cheng, C. Wan, and J. Liu, “Image quality classification for DR screening using deep learning,” In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2017), pp. 664–667.
  20. Y. Zhang, L. Wang, Z. Wu, J. Zeng, Y. Chen, R. Tian, J. Zhao, and G. Zhang, “Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images,” IEEE Access 7, 10232–10241 (2019).
    [Crossref]
  21. G. T. Zago, R. V. Andreão, B. Dorizzi, and E. O. T. Salles, “Retinal image quality assessment using deep learning,” Comput. Biol. Med. 103, 64–70 (2018).
    [Crossref]
  22. J. Kauer, K. Gawlik, H. G. Zimmermann, E. M. Kadas, C. Bereuter, and F. Paul, … & I. E. Beckers, “Automatic quality evaluation as assessment standard for optical coherence tomography,” In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVII, (2019, February), Vol. 10868, p. 1086814. International Society for Optics and Photonics.
  23. M. Zhang, J. Y. Wang, L. Zhang, J. Feng, and Y. Lv, “Deep residual-network-based quality assessment for SD-OCT retinal images: preliminary study,” In Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, (2019, March), Vol. 10952, p. 1095214. International Society for Optics and Photonics.
  24. A. Courville, I. Goodfellow, and Y. Bengio, “Deep Learning Book,” Deep learning, 21(1), 111–124. arXiv:arXiv:1011. (2015).
  25. 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]
  26. S. Liu, W. Cai, S. Pujol, R. Kikinis, and D. Feng, “Early Diagnosis of Alzheimer’s Disease with Deep Learning,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Beijing, (2014), pp. 1015–1018.
  27. D. Maji, A. Santara, P. Mitra, and D. Sheet, “Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images,” arXiv preprint arXiv:1603.04833. (2016).
  28. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556. (2014).
  29. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 2818–2826.
  30. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 770–778.
  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]
  32. 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]
  33. J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” IEEE conference on computer vision and pattern recognition (2009). pp. 248–255.
  34. L. Bottou, “Large-scale machine learning with stochastic gradient descent,” In Proceedings of COMPSTAT”2010, (2009, June), pp. 177–186. Physica-Verlag HD.

2019 (1)

Y. Zhang, L. Wang, Z. Wu, J. Zeng, Y. Chen, R. Tian, J. Zhao, and G. Zhang, “Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images,” IEEE Access 7, 10232–10241 (2019).
[Crossref]

2018 (4)

G. T. Zago, R. V. Andreão, B. Dorizzi, and E. O. T. Salles, “Retinal image quality assessment using deep learning,” Comput. Biol. Med. 103, 64–70 (2018).
[Crossref]

R. Rasti, H. Rabbani, A. Mehridehnavi, and F. Hajizadeh, “Macular OCT classification using a multi-scale convolutional neural network ensemble,” IEEE Trans. Med. Imaging 37(4), 1024–1034 (2018).
[Crossref]

M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefe’s Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
[Crossref]

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

2017 (4)

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]

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]

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]

E. A. Swanson and J. G. Fujimoto, “The ecosystem that powered the translation of OCT from fundamental research to clinical and commercial impact,” Biomed. Opt. Express 8(3), 1638–1664 (2017).
[Crossref]

2016 (1)

2014 (1)

S. Farsiu, S. J. Chiu, R. V. O’ Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref]

2013 (3)

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
[Crossref]

X. Gao, F. Gao, D. Tao, and X. Li, “Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning,” IEEE Trans. Neural Netw. Learning Syst. 24(12), 2013–2026 (2013).
[Crossref]

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]

2012 (1)

Y. Huang, S. Gangaputra, K. E. Lee, A. R. Narkar, R. Klein, B. E. K. Klein, S. M. Meuer, and R. P. Danis, “Signal Quality Assessment of Retinal Optical Coherence Tomography Images. Signal quality assessment of retinal optical coherence tomography images,” Invest. Ophthalmol. Visual Sci. 53(4), 2133–2141 (2012).
[Crossref]

2007 (1)

M. E. Velthoven, D. J. Faber, F. D. Verbraak, T. G. van Leeuwen, and M. D. de Smet, “Recent developments in optical coherence tomography for imaging the retina,” Prog. Retinal Eye Res. 26(1), 57–77 (2007).
[Crossref]

2006 (1)

D. M. Stein, H. Ishikawa, R. Hariprasad, G. Wollstein, R. J. Noecker, J. G. Fujimoto, and J. S. Schuman, “A new quality assessment parameter for optical coherence tomography,” Br. J. Ophthalmol. 90(2), 186–190 (2006).
[Crossref]

2004 (1)

H. Ishikawa, G. Wollstein, M. Aoyama, D. Stein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Stratus OCT image quality assessment,” Invest. Ophthalmol. Visual Sci. 45(13), 3317 (2004).

Acharya, U. R.

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]

Andreão, R. V.

G. T. Zago, R. V. Andreão, B. Dorizzi, and E. O. T. Salles, “Retinal image quality assessment using deep learning,” Comput. Biol. Med. 103, 64–70 (2018).
[Crossref]

Anthony Lewis, M.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Aoyama, M.

H. Ishikawa, G. Wollstein, M. Aoyama, D. Stein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Stratus OCT image quality assessment,” Invest. Ophthalmol. Visual Sci. 45(13), 3317 (2004).

Baxter, S. L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Beaton, S.

H. Ishikawa, G. Wollstein, M. Aoyama, D. Stein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Stratus OCT image quality assessment,” Invest. Ophthalmol. Visual Sci. 45(13), 3317 (2004).

Beckers, I. E.

J. Kauer, K. Gawlik, H. G. Zimmermann, E. M. Kadas, C. Bereuter, and F. Paul, … & I. E. Beckers, “Automatic quality evaluation as assessment standard for optical coherence tomography,” In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVII, (2019, February), Vol. 10868, p. 1086814. International Society for Optics and Photonics.

Bengio, Y.

A. Courville, I. Goodfellow, and Y. Bengio, “Deep Learning Book,” Deep learning, 21(1), 111–124. arXiv:arXiv:1011. (2015).

Bereuter, C.

J. Kauer, K. Gawlik, H. G. Zimmermann, E. M. Kadas, C. Bereuter, and F. Paul, … & I. E. Beckers, “Automatic quality evaluation as assessment standard for optical coherence tomography,” In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVII, (2019, February), Vol. 10868, p. 1086814. International Society for Optics and Photonics.

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]

Bottou, L.

L. Bottou, “Large-scale machine learning with stochastic gradient descent,” In Proceedings of COMPSTAT”2010, (2009, June), pp. 177–186. Physica-Verlag HD.

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]

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]

Cai, W.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

S. Liu, W. Cai, S. Pujol, R. Kikinis, and D. Feng, “Early Diagnosis of Alzheimer’s Disease with Deep Learning,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Beijing, (2014), pp. 1015–1018.

Calabresi, P. A.

Carass, A.

Chakraborty, D.

Chatterjee, J.

Chen, Y.

Y. Zhang, L. Wang, Z. Wu, J. Zeng, Y. Chen, R. Tian, J. Zhao, and G. Zhang, “Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images,” IEEE Access 7, 10232–10241 (2019).
[Crossref]

Cheng, J.

J. Sun, C. Wan, J. Cheng, F. Yu, and J. Liu, “Retinal image quality classification using fine-tuned CNN,” In Fetal, Infant and Ophthalmic Medical Image Analysis, (2017), pp. 126–133. Springer, Cham.

F. Yu, J. Sun, A. Li, J. Cheng, C. Wan, and J. Liu, “Image quality classification for DR screening using deep learning,” In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2017), pp. 664–667.

Chiu, S. J.

S. Farsiu, S. J. Chiu, R. V. O’ Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref]

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]

Courville, A.

A. Courville, I. Goodfellow, and Y. Bengio, “Deep Learning Book,” Deep learning, 21(1), 111–124. arXiv:arXiv:1011. (2015).

Cuadros, J.

S. K. Saha, B. Fernando, J. Cuadros, D. Xiao, and Y. Kanagasingam, “Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening,” arXiv preprint arXiv:1703.02511. (2017).

Danis, R. P.

Y. Huang, S. Gangaputra, K. E. Lee, A. R. Narkar, R. Klein, B. E. K. Klein, S. M. Meuer, and R. P. Danis, “Signal Quality Assessment of Retinal Optical Coherence Tomography Images. Signal quality assessment of retinal optical coherence tomography images,” Invest. Ophthalmol. Visual Sci. 53(4), 2133–2141 (2012).
[Crossref]

de Smet, M. D.

M. E. Velthoven, D. J. Faber, F. D. Verbraak, T. G. van Leeuwen, and M. D. de Smet, “Recent developments in optical coherence tomography for imaging the retina,” Prog. Retinal Eye Res. 26(1), 57–77 (2007).
[Crossref]

Deng, J.

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” IEEE conference on computer vision and pattern recognition (2009). pp. 248–255.

Dewelle, J.

S. Liu, A. S. Paranjape, B. Elmaanaoui, J. Dewelle, H. G. Rylander, M. K. Markey, and T. E. Milner, “Quality assessment for spectral domain optical coherence tomography (OCT) images,” In Multimodal Biomedical Imaging IV (Vol. 7171, p. 71710X). International Society for Optics and Photonics (2009).

Dong, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Dong, W.

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” IEEE conference on computer vision and pattern recognition (2009). pp. 248–255.

Dorizzi, B.

G. T. Zago, R. V. Andreão, B. Dorizzi, and E. O. T. Salles, “Retinal image quality assessment using deep learning,” Comput. Biol. Med. 103, 64–70 (2018).
[Crossref]

Duan, Y.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Elmaanaoui, B.

S. Liu, A. S. Paranjape, B. Elmaanaoui, J. Dewelle, H. G. Rylander, M. K. Markey, and T. E. Milner, “Quality assessment for spectral domain optical coherence tomography (OCT) images,” In Multimodal Biomedical Imaging IV (Vol. 7171, p. 71710X). International Society for Optics and Photonics (2009).

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]

Eter, N.

M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefe’s Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
[Crossref]

Faber, D. J.

M. E. Velthoven, D. J. Faber, F. D. Verbraak, T. G. van Leeuwen, and M. D. de Smet, “Recent developments in optical coherence tomography for imaging the retina,” Prog. Retinal Eye Res. 26(1), 57–77 (2007).
[Crossref]

Farsiu, S.

S. Farsiu, S. J. Chiu, R. V. O’ Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref]

Fei-Fei, L.

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” IEEE conference on computer vision and pattern recognition (2009). pp. 248–255.

Feng, D.

S. Liu, W. Cai, S. Pujol, R. Kikinis, and D. Feng, “Early Diagnosis of Alzheimer’s Disease with Deep Learning,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Beijing, (2014), pp. 1015–1018.

Feng, J.

M. Zhang, J. Y. Wang, L. Zhang, J. Feng, and Y. Lv, “Deep residual-network-based quality assessment for SD-OCT retinal images: preliminary study,” In Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, (2019, March), Vol. 10952, p. 1095214. International Society for Optics and Photonics.

Fernando, B.

S. K. Saha, B. Fernando, J. Cuadros, D. Xiao, and Y. Kanagasingam, “Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening,” arXiv preprint arXiv:1703.02511. (2017).

Folgar, F. A.

S. Farsiu, S. J. Chiu, R. V. O’ Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref]

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]

Fu, X.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Fujimoto, J. G.

E. A. Swanson and J. G. Fujimoto, “The ecosystem that powered the translation of OCT from fundamental research to clinical and commercial impact,” Biomed. Opt. Express 8(3), 1638–1664 (2017).
[Crossref]

D. M. Stein, H. Ishikawa, R. Hariprasad, G. Wollstein, R. J. Noecker, J. G. Fujimoto, and J. S. Schuman, “A new quality assessment parameter for optical coherence tomography,” Br. J. Ophthalmol. 90(2), 186–190 (2006).
[Crossref]

H. Ishikawa, G. Wollstein, M. Aoyama, D. Stein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Stratus OCT image quality assessment,” Invest. Ophthalmol. Visual Sci. 45(13), 3317 (2004).

Gangaputra, S.

Y. Huang, S. Gangaputra, K. E. Lee, A. R. Narkar, R. Klein, B. E. K. Klein, S. M. Meuer, and R. P. Danis, “Signal Quality Assessment of Retinal Optical Coherence Tomography Images. Signal quality assessment of retinal optical coherence tomography images,” Invest. Ophthalmol. Visual Sci. 53(4), 2133–2141 (2012).
[Crossref]

Gao, F.

X. Gao, F. Gao, D. Tao, and X. Li, “Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning,” IEEE Trans. Neural Netw. Learning Syst. 24(12), 2013–2026 (2013).
[Crossref]

Gao, X.

X. Gao, F. Gao, D. Tao, and X. Li, “Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning,” IEEE Trans. Neural Netw. Learning Syst. 24(12), 2013–2026 (2013).
[Crossref]

Garnavi, R.

R. Tennakoon, D. Mahapatra, P. Roy, S. Sedai, and R. Garnavi, “Image Quality Classification for DR Screening Using Convolutional Neural Networks,” In: X. Chen, M. K. Garvin, J. Liu, E. Trucco, and Y. Xu, eds. Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, OMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, (October 21, 2016), 113–120.

Gawlik, K.

J. Kauer, K. Gawlik, H. G. Zimmermann, E. M. Kadas, C. Bereuter, and F. Paul, … & I. E. Beckers, “Automatic quality evaluation as assessment standard for optical coherence tomography,” In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVII, (2019, February), Vol. 10868, p. 1086814. International Society for Optics and Photonics.

Goldbaum, M.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Goodfellow, I.

A. Courville, I. Goodfellow, and Y. Bengio, “Deep Learning Book,” Deep learning, 21(1), 111–124. arXiv:arXiv:1011. (2015).

Gu, K.

K. Gu, G. Zhai, X. Yang, and W. Zhang, “Deep learning network for blind image quality assessment,” In 2014 IEEE International Conference on Image Processing (ICIP), (2014, October), pp. 511–515. IEEE.

Hajizadeh, F.

R. Rasti, H. Rabbani, A. Mehridehnavi, and F. Hajizadeh, “Macular OCT classification using a multi-scale convolutional neural network ensemble,” IEEE Trans. Med. Imaging 37(4), 1024–1034 (2018).
[Crossref]

Hariprasad, R.

D. M. Stein, H. Ishikawa, R. Hariprasad, G. Wollstein, R. J. Noecker, J. G. Fujimoto, and J. S. Schuman, “A new quality assessment parameter for optical coherence tomography,” Br. J. Ophthalmol. 90(2), 186–190 (2006).
[Crossref]

Hauser, M.

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 770–778.

Hewett, S.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Hou, R.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Huang, Y.

Y. Huang, S. Gangaputra, K. E. Lee, A. R. Narkar, R. Klein, B. E. K. Klein, S. M. Meuer, and R. P. Danis, “Signal Quality Assessment of Retinal Optical Coherence Tomography Images. Signal quality assessment of retinal optical coherence tomography images,” Invest. Ophthalmol. Visual Sci. 53(4), 2133–2141 (2012).
[Crossref]

Huu, V. A. N.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Ioffe, S.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 2818–2826.

Ishikawa, H.

D. M. Stein, H. Ishikawa, R. Hariprasad, G. Wollstein, R. J. Noecker, J. G. Fujimoto, and J. S. Schuman, “A new quality assessment parameter for optical coherence tomography,” Br. J. Ophthalmol. 90(2), 186–190 (2006).
[Crossref]

H. Ishikawa, G. Wollstein, M. Aoyama, D. Stein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Stratus OCT image quality assessment,” Invest. Ophthalmol. Visual Sci. 45(13), 3317 (2004).

Izatt, J. A.

S. Farsiu, S. J. Chiu, R. V. O’ Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref]

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]

Kadas, E. M.

J. Kauer, K. Gawlik, H. G. Zimmermann, E. M. Kadas, C. Bereuter, and F. Paul, … & I. E. Beckers, “Automatic quality evaluation as assessment standard for optical coherence tomography,” In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVII, (2019, February), Vol. 10868, p. 1086814. International Society for Optics and Photonics.

Kanagasingam, Y.

S. K. Saha, B. Fernando, J. Cuadros, D. Xiao, and Y. Kanagasingam, “Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening,” arXiv preprint arXiv:1703.02511. (2017).

Karri, S. P. K.

Kauer, J.

J. Kauer, K. Gawlik, H. G. Zimmermann, E. M. Kadas, C. Bereuter, and F. Paul, … & I. E. Beckers, “Automatic quality evaluation as assessment standard for optical coherence tomography,” In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVII, (2019, February), Vol. 10868, p. 1086814. International Society for Optics and Photonics.

Kermany, D. S.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Kikinis, R.

S. Liu, W. Cai, S. Pujol, R. Kikinis, and D. Feng, “Early Diagnosis of Alzheimer’s Disease with Deep Learning,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Beijing, (2014), pp. 1015–1018.

Klein, B. E. K.

Y. Huang, S. Gangaputra, K. E. Lee, A. R. Narkar, R. Klein, B. E. K. Klein, S. M. Meuer, and R. P. Danis, “Signal Quality Assessment of Retinal Optical Coherence Tomography Images. Signal quality assessment of retinal optical coherence tomography images,” Invest. Ophthalmol. Visual Sci. 53(4), 2133–2141 (2012).
[Crossref]

Klein, R.

Y. Huang, S. Gangaputra, K. E. Lee, A. R. Narkar, R. Klein, B. E. K. Klein, S. M. Meuer, and R. P. Danis, “Signal Quality Assessment of Retinal Optical Coherence Tomography Images. Signal quality assessment of retinal optical coherence tomography images,” Invest. Ophthalmol. Visual Sci. 53(4), 2133–2141 (2012).
[Crossref]

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]

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]

Lang, A.

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]

Lauermann, J. L.

M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefe’s Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
[Crossref]

Lee, K. E.

Y. Huang, S. Gangaputra, K. E. Lee, A. R. Narkar, R. Klein, B. E. K. Klein, S. M. Meuer, and R. P. Danis, “Signal Quality Assessment of Retinal Optical Coherence Tomography Images. Signal quality assessment of retinal optical coherence tomography images,” Invest. Ophthalmol. Visual Sci. 53(4), 2133–2141 (2012).
[Crossref]

Li, A.

F. Yu, J. Sun, A. Li, J. Cheng, C. Wan, and J. Liu, “Image quality classification for DR screening using deep learning,” In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2017), pp. 664–667.

Li, C.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Li, K.

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” IEEE conference on computer vision and pattern recognition (2009). pp. 248–255.

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,” IEEE conference on computer vision and pattern recognition (2009). pp. 248–255.

Li, O.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Li, X.

X. Gao, F. Gao, D. Tao, and X. Li, “Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning,” IEEE Trans. Neural Netw. Learning Syst. 24(12), 2013–2026 (2013).
[Crossref]

Liang, H.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

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]

Liu, J.

J. Sun, C. Wan, J. Cheng, F. Yu, and J. Liu, “Retinal image quality classification using fine-tuned CNN,” In Fetal, Infant and Ophthalmic Medical Image Analysis, (2017), pp. 126–133. Springer, Cham.

F. Yu, J. Sun, A. Li, J. Cheng, C. Wan, and J. Liu, “Image quality classification for DR screening using deep learning,” In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2017), pp. 664–667.

Liu, S.

S. Liu, W. Cai, S. Pujol, R. Kikinis, and D. Feng, “Early Diagnosis of Alzheimer’s Disease with Deep Learning,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Beijing, (2014), pp. 1015–1018.

S. Liu, A. S. Paranjape, B. Elmaanaoui, J. Dewelle, H. G. Rylander, M. K. Markey, and T. E. Milner, “Quality assessment for spectral domain optical coherence tomography (OCT) images,” In Multimodal Biomedical Imaging IV (Vol. 7171, p. 71710X). International Society for Optics and Photonics (2009).

Lv, Y.

M. Zhang, J. Y. Wang, L. Zhang, J. Feng, and Y. Lv, “Deep residual-network-based quality assessment for SD-OCT retinal images: preliminary study,” In Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, (2019, March), Vol. 10952, p. 1095214. International Society for Optics and Photonics.

Mahapatra, D.

R. Tennakoon, D. Mahapatra, P. Roy, S. Sedai, and R. Garnavi, “Image Quality Classification for DR Screening Using Convolutional Neural Networks,” In: X. Chen, M. K. Garvin, J. Liu, E. Trucco, and Y. Xu, eds. Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, OMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, (October 21, 2016), 113–120.

Maji, D.

D. Maji, A. Santara, P. Mitra, and D. Sheet, “Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images,” arXiv preprint arXiv:1603.04833. (2016).

Markey, M. K.

S. Liu, A. S. Paranjape, B. Elmaanaoui, J. Dewelle, H. G. Rylander, M. K. Markey, and T. E. Milner, “Quality assessment for spectral domain optical coherence tomography (OCT) images,” In Multimodal Biomedical Imaging IV (Vol. 7171, p. 71710X). International Society for Optics and Photonics (2009).

McKeown, A.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Mehridehnavi, A.

R. Rasti, H. Rabbani, A. Mehridehnavi, and F. Hajizadeh, “Macular OCT classification using a multi-scale convolutional neural network ensemble,” IEEE Trans. Med. Imaging 37(4), 1024–1034 (2018).
[Crossref]

Meuer, S. M.

Y. Huang, S. Gangaputra, K. E. Lee, A. R. Narkar, R. Klein, B. E. K. Klein, S. M. Meuer, and R. P. Danis, “Signal Quality Assessment of Retinal Optical Coherence Tomography Images. Signal quality assessment of retinal optical coherence tomography images,” Invest. Ophthalmol. Visual Sci. 53(4), 2133–2141 (2012).
[Crossref]

Milner, T. E.

S. Liu, A. S. Paranjape, B. Elmaanaoui, J. Dewelle, H. G. Rylander, M. K. Markey, and T. E. Milner, “Quality assessment for spectral domain optical coherence tomography (OCT) images,” In Multimodal Biomedical Imaging IV (Vol. 7171, p. 71710X). International Society for Optics and Photonics (2009).

Mitra, P.

D. Maji, A. Santara, P. Mitra, and D. Sheet, “Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images,” arXiv preprint arXiv:1603.04833. (2016).

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]

Narkar, A. R.

Y. Huang, S. Gangaputra, K. E. Lee, A. R. Narkar, R. Klein, B. E. K. Klein, S. M. Meuer, and R. P. Danis, “Signal Quality Assessment of Retinal Optical Coherence Tomography Images. Signal quality assessment of retinal optical coherence tomography images,” Invest. Ophthalmol. Visual Sci. 53(4), 2133–2141 (2012).
[Crossref]

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]

Noecker, R. J.

D. M. Stein, H. Ishikawa, R. Hariprasad, G. Wollstein, R. J. Noecker, J. G. Fujimoto, and J. S. Schuman, “A new quality assessment parameter for optical coherence tomography,” Br. J. Ophthalmol. 90(2), 186–190 (2006).
[Crossref]

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]

O’ Connell, R. V.

S. Farsiu, S. J. Chiu, R. V. O’ Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref]

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]

Paranjape, A. S.

S. Liu, A. S. Paranjape, B. Elmaanaoui, J. Dewelle, H. G. Rylander, M. K. Markey, and T. E. Milner, “Quality assessment for spectral domain optical coherence tomography (OCT) images,” In Multimodal Biomedical Imaging IV (Vol. 7171, p. 71710X). International Society for Optics and Photonics (2009).

Paul, F.

J. Kauer, K. Gawlik, H. G. Zimmermann, E. M. Kadas, C. Bereuter, and F. Paul, … & I. E. Beckers, “Automatic quality evaluation as assessment standard for optical coherence tomography,” In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVII, (2019, February), Vol. 10868, p. 1086814. International Society for Optics and Photonics.

Pei, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[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]

Prasadha, M. K.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Prince, J. L.

Pujol, S.

S. Liu, W. Cai, S. Pujol, R. Kikinis, and D. Feng, “Early Diagnosis of Alzheimer’s Disease with Deep Learning,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Beijing, (2014), pp. 1015–1018.

Rabbani, H.

R. Rasti, H. Rabbani, A. Mehridehnavi, and F. Hajizadeh, “Macular OCT classification using a multi-scale convolutional neural network ensemble,” IEEE Trans. Med. Imaging 37(4), 1024–1034 (2018).
[Crossref]

Rasti, R.

R. Rasti, H. Rabbani, A. Mehridehnavi, and F. Hajizadeh, “Macular OCT classification using a multi-scale convolutional neural network ensemble,” IEEE Trans. Med. Imaging 37(4), 1024–1034 (2018).
[Crossref]

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 770–778.

Roy, P.

R. Tennakoon, D. Mahapatra, P. Roy, S. Sedai, and R. Garnavi, “Image Quality Classification for DR Screening Using Convolutional Neural Networks,” In: X. Chen, M. K. Garvin, J. Liu, E. Trucco, and Y. Xu, eds. Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, OMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, (October 21, 2016), 113–120.

Rylander, H. G.

S. Liu, A. S. Paranjape, B. Elmaanaoui, J. Dewelle, H. G. Rylander, M. K. Markey, and T. E. Milner, “Quality assessment for spectral domain optical coherence tomography (OCT) images,” In Multimodal Biomedical Imaging IV (Vol. 7171, p. 71710X). International Society for Optics and Photonics (2009).

Saha, S. K.

S. K. Saha, B. Fernando, J. Cuadros, D. Xiao, and Y. Kanagasingam, “Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening,” arXiv preprint arXiv:1703.02511. (2017).

Salles, E. O. T.

G. T. Zago, R. V. Andreão, B. Dorizzi, and E. O. T. Salles, “Retinal image quality assessment using deep learning,” Comput. Biol. Med. 103, 64–70 (2018).
[Crossref]

Santara, A.

D. Maji, A. Santara, P. Mitra, and D. Sheet, “Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images,” arXiv preprint arXiv:1603.04833. (2016).

Schuman, J. S.

D. M. Stein, H. Ishikawa, R. Hariprasad, G. Wollstein, R. J. Noecker, J. G. Fujimoto, and J. S. Schuman, “A new quality assessment parameter for optical coherence tomography,” Br. J. Ophthalmol. 90(2), 186–190 (2006).
[Crossref]

H. Ishikawa, G. Wollstein, M. Aoyama, D. Stein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Stratus OCT image quality assessment,” Invest. Ophthalmol. Visual Sci. 45(13), 3317 (2004).

Sedai, S.

R. Tennakoon, D. Mahapatra, P. Roy, S. Sedai, and R. Garnavi, “Image Quality Classification for DR Screening Using Convolutional Neural Networks,” In: X. Chen, M. K. Garvin, J. Liu, E. Trucco, and Y. Xu, eds. Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, OMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, (October 21, 2016), 113–120.

Sheet, D.

D. Maji, A. Santara, P. Mitra, and D. Sheet, “Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images,” arXiv preprint arXiv:1603.04833. (2016).

Shi, A.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Shi, W.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Shlens, J.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 2818–2826.

Simonyan, K.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556. (2014).

Singer, M. A.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Socher, R.

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” IEEE conference on computer vision and pattern recognition (2009). pp. 248–255.

Sotirchos, E. S.

Stein, D.

H. Ishikawa, G. Wollstein, M. Aoyama, D. Stein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Stratus OCT image quality assessment,” Invest. Ophthalmol. Visual Sci. 45(13), 3317 (2004).

Stein, D. M.

D. M. Stein, H. Ishikawa, R. Hariprasad, G. Wollstein, R. J. Noecker, J. G. Fujimoto, and J. S. Schuman, “A new quality assessment parameter for optical coherence tomography,” Br. J. Ophthalmol. 90(2), 186–190 (2006).
[Crossref]

Sun, J.

J. Sun, C. Wan, J. Cheng, F. Yu, and J. Liu, “Retinal image quality classification using fine-tuned CNN,” In Fetal, Infant and Ophthalmic Medical Image Analysis, (2017), pp. 126–133. Springer, Cham.

F. Yu, J. Sun, A. Li, J. Cheng, C. Wan, and J. Liu, “Image quality classification for DR screening using deep learning,” In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2017), pp. 664–667.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 770–778.

Sun, X.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Swanson, E. A.

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]

Szegedy, C.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 2818–2826.

Tafreshi, A.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Tao, D.

X. Gao, F. Gao, D. Tao, and X. Li, “Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning,” IEEE Trans. Neural Netw. Learning Syst. 24(12), 2013–2026 (2013).
[Crossref]

Tennakoon, R.

R. Tennakoon, D. Mahapatra, P. Roy, S. Sedai, and R. Garnavi, “Image Quality Classification for DR Screening Using Convolutional Neural Networks,” In: X. Chen, M. K. Garvin, J. Liu, E. Trucco, and Y. Xu, eds. Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, OMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, (October 21, 2016), 113–120.

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]

Tian, R.

Y. Zhang, L. Wang, Z. Wu, J. Zeng, Y. Chen, R. Tian, J. Zhao, and G. Zhang, “Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images,” IEEE Access 7, 10232–10241 (2019).
[Crossref]

Ting, M. Y. L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Toth, C. A.

S. Farsiu, S. J. Chiu, R. V. O’ Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref]

Treder, M.

M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefe’s Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
[Crossref]

Valentim, C. C. S.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

van Leeuwen, T. G.

M. E. Velthoven, D. J. Faber, F. D. Verbraak, T. G. van Leeuwen, and M. D. de Smet, “Recent developments in optical coherence tomography for imaging the retina,” Prog. Retinal Eye Res. 26(1), 57–77 (2007).
[Crossref]

Vanhoucke, V.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 2818–2826.

Velthoven, M. E.

M. E. Velthoven, D. J. Faber, F. D. Verbraak, T. G. van Leeuwen, and M. D. de Smet, “Recent developments in optical coherence tomography for imaging the retina,” Prog. Retinal Eye Res. 26(1), 57–77 (2007).
[Crossref]

Verbraak, F. D.

M. E. Velthoven, D. J. Faber, F. D. Verbraak, T. G. van Leeuwen, and M. D. de Smet, “Recent developments in optical coherence tomography for imaging the retina,” Prog. Retinal Eye Res. 26(1), 57–77 (2007).
[Crossref]

Wan, C.

F. Yu, J. Sun, A. Li, J. Cheng, C. Wan, and J. Liu, “Image quality classification for DR screening using deep learning,” In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2017), pp. 664–667.

J. Sun, C. Wan, J. Cheng, F. Yu, and J. Liu, “Retinal image quality classification using fine-tuned CNN,” In Fetal, Infant and Ophthalmic Medical Image Analysis, (2017), pp. 126–133. Springer, Cham.

Wang, J. Y.

M. Zhang, J. Y. Wang, L. Zhang, J. Feng, and Y. Lv, “Deep residual-network-based quality assessment for SD-OCT retinal images: preliminary study,” In Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, (2019, March), Vol. 10952, p. 1095214. International Society for Optics and Photonics.

Wang, L.

Y. Zhang, L. Wang, Z. Wu, J. Zeng, Y. Chen, R. Tian, J. Zhao, and G. Zhang, “Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images,” IEEE Access 7, 10232–10241 (2019).
[Crossref]

Wang, X.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Wang, Y.

Wen, C.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Wojna, Z.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 2818–2826.

Wollstein, G.

D. M. Stein, H. Ishikawa, R. Hariprasad, G. Wollstein, R. J. Noecker, J. G. Fujimoto, and J. S. Schuman, “A new quality assessment parameter for optical coherence tomography,” Br. J. Ophthalmol. 90(2), 186–190 (2006).
[Crossref]

H. Ishikawa, G. Wollstein, M. Aoyama, D. Stein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Stratus OCT image quality assessment,” Invest. Ophthalmol. Visual Sci. 45(13), 3317 (2004).

Wu, X.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Wu, Z.

Y. Zhang, L. Wang, Z. Wu, J. Zeng, Y. Chen, R. Tian, J. Zhao, and G. Zhang, “Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images,” IEEE Access 7, 10232–10241 (2019).
[Crossref]

Xia, H.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Xiao, D.

S. K. Saha, B. Fernando, J. Cuadros, D. Xiao, and Y. Kanagasingam, “Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening,” arXiv preprint arXiv:1703.02511. (2017).

Xu, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Yan, F.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Yang, G.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Yang, X.

K. Gu, G. Zhai, X. Yang, and W. Zhang, “Deep learning network for blind image quality assessment,” In 2014 IEEE International Conference on Image Processing (ICIP), (2014, October), pp. 511–515. IEEE.

Yao, Z.

Ying, H. S.

Yu, F.

J. Sun, C. Wan, J. Cheng, F. Yu, and J. Liu, “Retinal image quality classification using fine-tuned CNN,” In Fetal, Infant and Ophthalmic Medical Image Analysis, (2017), pp. 126–133. Springer, Cham.

F. Yu, J. Sun, A. Li, J. Cheng, C. Wan, and J. Liu, “Image quality classification for DR screening using deep learning,” In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2017), pp. 664–667.

Yuan, E.

S. Farsiu, S. J. Chiu, R. V. O’ Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref]

Zago, G. T.

G. T. Zago, R. V. Andreão, B. Dorizzi, and E. O. T. Salles, “Retinal image quality assessment using deep learning,” Comput. Biol. Med. 103, 64–70 (2018).
[Crossref]

Zeng, J.

Y. Zhang, L. Wang, Z. Wu, J. Zeng, Y. Chen, R. Tian, J. Zhao, and G. Zhang, “Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images,” IEEE Access 7, 10232–10241 (2019).
[Crossref]

Zhai, G.

K. Gu, G. Zhai, X. Yang, and W. Zhang, “Deep learning network for blind image quality assessment,” In 2014 IEEE International Conference on Image Processing (ICIP), (2014, October), pp. 511–515. IEEE.

Zhang, C. L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Zhang, E. D.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Zhang, G.

Y. Zhang, L. Wang, Z. Wu, J. Zeng, Y. Chen, R. Tian, J. Zhao, and G. Zhang, “Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images,” IEEE Access 7, 10232–10241 (2019).
[Crossref]

Zhang, K.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Zhang, L.

M. Zhang, J. Y. Wang, L. Zhang, J. Feng, and Y. Lv, “Deep residual-network-based quality assessment for SD-OCT retinal images: preliminary study,” In Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, (2019, March), Vol. 10952, p. 1095214. International Society for Optics and Photonics.

Zhang, M.

M. Zhang, J. Y. Wang, L. Zhang, J. Feng, and Y. Lv, “Deep residual-network-based quality assessment for SD-OCT retinal images: preliminary study,” In Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, (2019, March), Vol. 10952, p. 1095214. International Society for Optics and Photonics.

Zhang, R.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Zhang, W.

K. Gu, G. Zhai, X. Yang, and W. Zhang, “Deep learning network for blind image quality assessment,” In 2014 IEEE International Conference on Image Processing (ICIP), (2014, October), pp. 511–515. IEEE.

Zhang, X.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 770–778.

Zhang, Y.

Y. Zhang, L. Wang, Z. Wu, J. Zeng, Y. Chen, R. Tian, J. Zhao, and G. Zhang, “Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images,” IEEE Access 7, 10232–10241 (2019).
[Crossref]

Y. Wang, Y. Zhang, Z. Yao, R. Zhao, and F. Zhou, “Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images,” Biomed. Opt. Express 7(12), 4928–4940 (2016).
[Crossref]

Zhao, J.

Y. Zhang, L. Wang, Z. Wu, J. Zeng, Y. Chen, R. Tian, J. Zhao, and G. Zhang, “Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images,” IEEE Access 7, 10232–10241 (2019).
[Crossref]

Zhao, R.

Zheng, L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Zhou, F.

Zhu, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Zimmermann, H. G.

J. Kauer, K. Gawlik, H. G. Zimmermann, E. M. Kadas, C. Bereuter, and F. Paul, … & I. E. Beckers, “Automatic quality evaluation as assessment standard for optical coherence tomography,” In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVII, (2019, February), Vol. 10868, p. 1086814. International Society for Optics and Photonics.

Zisserman, A.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556. (2014).

Ziyar, I.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Biomed. Opt. Express (4)

Br. J. Ophthalmol. (1)

D. M. Stein, H. Ishikawa, R. Hariprasad, G. Wollstein, R. J. Noecker, J. G. Fujimoto, and J. S. Schuman, “A new quality assessment parameter for optical coherence tomography,” Br. J. Ophthalmol. 90(2), 186–190 (2006).
[Crossref]

Cell (1)

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. Anthony Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

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]

G. T. Zago, R. V. Andreão, B. Dorizzi, and E. O. T. Salles, “Retinal image quality assessment using deep learning,” Comput. Biol. Med. 103, 64–70 (2018).
[Crossref]

Graefe’s Arch. Clin. Exp. Ophthalmol. (1)

M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefe’s Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
[Crossref]

IEEE Access (1)

Y. Zhang, L. Wang, Z. Wu, J. Zeng, Y. Chen, R. Tian, J. Zhao, and G. Zhang, “Development of an Automated Screening System for Retinopathy of Prematurity Using a Deep Neural Network for Wide-angle Retinal Images,” IEEE Access 7, 10232–10241 (2019).
[Crossref]

IEEE Trans. Med. Imaging (1)

R. Rasti, H. Rabbani, A. Mehridehnavi, and F. Hajizadeh, “Macular OCT classification using a multi-scale convolutional neural network ensemble,” IEEE Trans. Med. Imaging 37(4), 1024–1034 (2018).
[Crossref]

IEEE Trans. Neural Netw. Learning Syst. (1)

X. Gao, F. Gao, D. Tao, and X. Li, “Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning,” IEEE Trans. Neural Netw. Learning Syst. 24(12), 2013–2026 (2013).
[Crossref]

Invest. Ophthalmol. Visual Sci. (2)

H. Ishikawa, G. Wollstein, M. Aoyama, D. Stein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Stratus OCT image quality assessment,” Invest. Ophthalmol. Visual Sci. 45(13), 3317 (2004).

Y. Huang, S. Gangaputra, K. E. Lee, A. R. Narkar, R. Klein, B. E. K. Klein, S. M. Meuer, and R. P. Danis, “Signal Quality Assessment of Retinal Optical Coherence Tomography Images. Signal quality assessment of retinal optical coherence tomography images,” Invest. Ophthalmol. Visual Sci. 53(4), 2133–2141 (2012).
[Crossref]

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]

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]

Ophthalmology (1)

S. Farsiu, S. J. Chiu, R. V. O’ Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref]

Prog. Retinal Eye Res. (1)

M. E. Velthoven, D. J. Faber, F. D. Verbraak, T. G. van Leeuwen, and M. D. de Smet, “Recent developments in optical coherence tomography for imaging the retina,” Prog. Retinal Eye Res. 26(1), 57–77 (2007).
[Crossref]

Other (16)

S. Liu, A. S. Paranjape, B. Elmaanaoui, J. Dewelle, H. G. Rylander, M. K. Markey, and T. E. Milner, “Quality assessment for spectral domain optical coherence tomography (OCT) images,” In Multimodal Biomedical Imaging IV (Vol. 7171, p. 71710X). International Society for Optics and Photonics (2009).

K. Gu, G. Zhai, X. Yang, and W. Zhang, “Deep learning network for blind image quality assessment,” In 2014 IEEE International Conference on Image Processing (ICIP), (2014, October), pp. 511–515. IEEE.

R. Tennakoon, D. Mahapatra, P. Roy, S. Sedai, and R. Garnavi, “Image Quality Classification for DR Screening Using Convolutional Neural Networks,” In: X. Chen, M. K. Garvin, J. Liu, E. Trucco, and Y. Xu, eds. Proceedings of the Ophthalmic Medical Image Analysis Third International Workshop, OMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, (October 21, 2016), 113–120.

S. K. Saha, B. Fernando, J. Cuadros, D. Xiao, and Y. Kanagasingam, “Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening,” arXiv preprint arXiv:1703.02511. (2017).

J. Sun, C. Wan, J. Cheng, F. Yu, and J. Liu, “Retinal image quality classification using fine-tuned CNN,” In Fetal, Infant and Ophthalmic Medical Image Analysis, (2017), pp. 126–133. Springer, Cham.

F. Yu, J. Sun, A. Li, J. Cheng, C. Wan, and J. Liu, “Image quality classification for DR screening using deep learning,” In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2017), pp. 664–667.

S. Liu, W. Cai, S. Pujol, R. Kikinis, and D. Feng, “Early Diagnosis of Alzheimer’s Disease with Deep Learning,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Beijing, (2014), pp. 1015–1018.

D. Maji, A. Santara, P. Mitra, and D. Sheet, “Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images,” arXiv preprint arXiv:1603.04833. (2016).

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556. (2014).

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 2818–2826.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In Proceedings of the IEEE conference on computer vision and pattern recognition, (2016), pp. 770–778.

J. Kauer, K. Gawlik, H. G. Zimmermann, E. M. Kadas, C. Bereuter, and F. Paul, … & I. E. Beckers, “Automatic quality evaluation as assessment standard for optical coherence tomography,” In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVII, (2019, February), Vol. 10868, p. 1086814. International Society for Optics and Photonics.

M. Zhang, J. Y. Wang, L. Zhang, J. Feng, and Y. Lv, “Deep residual-network-based quality assessment for SD-OCT retinal images: preliminary study,” In Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, (2019, March), Vol. 10952, p. 1095214. International Society for Optics and Photonics.

A. Courville, I. Goodfellow, and Y. Bengio, “Deep Learning Book,” Deep learning, 21(1), 111–124. arXiv:arXiv:1011. (2015).

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” IEEE conference on computer vision and pattern recognition (2009). pp. 248–255.

L. Bottou, “Large-scale machine learning with stochastic gradient descent,” In Proceedings of COMPSTAT”2010, (2009, June), pp. 177–186. Physica-Verlag HD.

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 (12)

Fig. 1.
Fig. 1. Examples of images with different qualities, including: (a) good, (b) off-center, (c) signal-shielded, and (d) other.
Fig. 2.
Fig. 2. Examples of normal and abnormal images, including: (a) the normal retina, (b) central serous retinopathy (CSR), (c) a macular hole, and (d) diabetic macular edema (DME).
Fig. 3.
Fig. 3. Residual blocks for (a) ResNet-18 and (b) ResNet-50
Fig. 4.
Fig. 4. The transfer learning architecture for OCT-IQA (optical coherence tomography image quality assessment) and retinopathy detection.
Fig. 5.
Fig. 5. Accuracy and loss curves for (a) VGG16, (b) ResNet-18, (c) ResNet-50, and (d) Inception-V3
Fig. 6.
Fig. 6. ROC curves and AUC values for five-fold cross-validation experiments on VGG16, ResNet-18, ResNet-50 and Inception-V3.
Fig. 7.
Fig. 7. A comparison of receiver operating characteristic (ROC) curves and AUC values for poor quality images (defined as off-center, signal-shielded, and other) using (a) VGG16 (AUC = 0.9896), (b) ResNet-18 (AUC = 0.9865), (c) ResNet-50 (AUC = 0.9947), and (d) Inception-V3 (AUC = 0.9898)
Fig. 8.
Fig. 8. Image quality heat maps showing (a) (b) off-center, (c) signal-shielded, and (d) ‘other’ categories.
Fig. 9.
Fig. 9. Five-fold cross-validation receiver operating characteristic (ROC) curves used to differentiate normal and abnormal retinas.
Fig. 10.
Fig. 10. AUC for retinopathy detection of (a) pure and (b) mixed datasets (right).
Fig. 11.
Fig. 11. Confusion matrices for (a) pure and (b) mixed retinopathy detection datasets.
Fig. 12.
Fig. 12. Heat maps for a retinopathy classifier showing: (a) a drusen structure, (b) thickening of the retina and pigment epithelial detachments (PED), (c) macular edema, and (d) retinal atrophy.

Tables (4)

Tables Icon

Table 1. A description of quality annotation standards.

Tables Icon

Table 2. Datasets used for training, evaluating, and testing the model

Tables Icon

Table 3. Accuracy, recall, specificity, precision and F1 score for different network architectures used for classification of image quality into “good,” “off-center,” “signal-shielded,” and “other.” The best option within each metric is in bold.

Tables Icon

Table 4. Test results for the retinopathy detection model with pure and mixed datasets.

Equations (8)

Equations on this page are rendered with MathJax. Learn more.

I = log 2 ( 1 + v i ) / log 2 ( v + 1 )
o ( x , y ) ( z + 1 ) = w = 0 W z - 1 h = 0 H z 1 s = 0 S z 1 k w , h , s z i x + w , y + h , s z ,
i z + 1 = F ( o z + 1 ) .
L = i g i log ( o i ) ,
A c c u r a c y = T P + T N T P + T N + F P + F N ,
S p e c i f i c i t y = T N F P + T N ,
R e c a l l = T P T P + F P ,
P r e c i s i o n = T P T P + F P .