Abstract

Diabetic retinopathy (DR) is a leading cause of blindness worldwide. However, 90% of DR caused blindness can be prevented if diagnosed and intervened early. Retinal exudates can be observed at the early stage of DR and can be used as signs for early DR diagnosis. Deep convolutional neural networks (DCNNs) have been applied for exudate detection with promising results. However, there exist two main challenges when applying the DCNN based methods for exudate detection. One is the very limited number of labeled data available from medical experts, and another is the severely imbalanced distribution of data of different classes. First, there are many more images of normal eyes than those of eyes with exudates, particularly for screening datasets. Second, the number of normal pixels (non-exudates) is much greater than the number of abnormal pixels (exudates) in images containing exudates. To tackle the small sample set problem, an ensemble convolutional neural network (MU-net) based on a U-net structure is presented in this paper. To alleviate the imbalance data problem, the conditional generative adversarial network (cGAN) is adopted to generate label-preserving minority class data specifically to implement the data augmentation. The network was trained on one dataset (e_ophtha_EX) and tested on the other three public datasets (DiaReTDB1, HEI-MED and MESSIDOR). CGAN, as a data augmentation method, significantly improves network robustness and generalization properties, achieving F1-scores of 92.79%, 92.46%, 91.27%, and 94.34%, respectively, as measured at the lesion level. While without cGAN, the corresponding F1-scores were 92.66%, 91.41%, 90.72%, and 90.58%, respectively. When measured at the image level, with cGAN we achieved the accuracy of 95.45%, 92.13%, 88.76%, and 89.58%, compared with the values achieved without cGAN of 86.36%, 87.64%, 76.33%, and 86.42%, respectively.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (1)

G. Douzas and F. Bacao, “Effective data generation for imbalanced learning using conditional generative adversarial networks,” Expert. Syst. with Appl. 91, 464–471 (2018).
[Crossref]

2017 (2)

J. H. Tan, H. Fujita, S. Sivaprasad, S. V. Bhandary, A. K. Rao, K. C. Chua, and U. R. Acharya, “Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network,” Inf. Sci. 420, 66–76 (2017).
[Crossref]

M. M. Fraz, W. Jahangir, S. Zahid, M. M. Hamayun, and S. A. Barman, “Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification,” Biomed. Signal Process. Control. 35, 50–62 (2017).
[Crossref]

2016 (2)

E. Imani and H.-R. Pourreza, “A novel method for retinal exudate segmentation using signal separation algorithm,” Comput. Methods Programs Biomed. 133, 195–205 (2016).
[Crossref] [PubMed]

P. Prentašić and S. Lončarić, “Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion,” Comput. Methods Programs Biomed. 137, 281–292 (2016).
[Crossref]

2015 (5)

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436 (2015).
[Crossref] [PubMed]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436 (2015).
[Crossref] [PubMed]

I. N. Figueiredo, S. Kumar, C. M. Oliveira, J. D. Ramos, and B. Engquist, “Automated lesion detectors in retinal fundus images,” Comput. Biol. Medicine 66, 47–65 (2015).
[Crossref]

K. Wisaeng, N. Hiransakolwong, and E. Pothiruk, “Automatic detection of exudates in retinal images based on threshold moving average models,” Biophysics 60, 288–297 (2015).
[Crossref]

C. Pereira, L. Gonçalves, and M. Ferreira, “Exudate segmentation in fundus images using an ant colony optimization approach,” Inf. Sci. 296, 14–24 (2015).
[Crossref]

2014 (5)

B. Harangi and A. Hajdu, “Automatic exudate detection by fusing multiple active contours and regionwise classification,” Comput. Biol. Medicine 54, 156–171 (2014).
[Crossref]

X. Zhang, G. Thibault, E. Decencière, B. Marcotegui, B. Laÿ, R. Danno, G. Cazuguel, G. Quellec, M. Lamard, P. Massin, and et al., “Exudate detection in color retinal images for mass screening of diabetic retinopathy,” Med. Image Analysis 18, 1026–1043 (2014).
[Crossref]

M. U. Akram, A. Tariq, S. A. Khan, and M. Y. Javed, “Automated detection of exudates and macula for grading of diabetic macular edema,” Comput. Methods Programs Biomed. 114, 141–152 (2014).
[Crossref] [PubMed]

C. Agurto, V. Murray, H. Yu, J. Wigdahl, M. Pattichis, S. Nemeth, E. S. Barriga, and P. Soliz, “A multiscale optimization approach to detect exudates in the macula,” IEEE J. Of Biomed. Heal. Informatics 18, 1328–1336 (2014).
[Crossref]

E. Decencière, X. Zhang, G. Cazuguel, B. Laÿ, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, and et al., “Feedback on a publicly distributed image database: the messidor database,” Image Analysis Stereol. 33, 231–234 (2014).
[Crossref]

2013 (3)

S. Ali, D. Sidibé, K. M. Adal, L. Giancardo, E. Chaum, T. P. Karnowski, and F. Mériaudeau, “Statistical atlas based exudate segmentation,” Comput. Med. Imaging Graph. 37, 358–368 (2013).
[Crossref] [PubMed]

L. Tang, M. Niemeijer, J. M. Reinhardt, M. K. Garvin, and M. D. Abramoff, “Splat feature classification with application to retinal hemorrhage detection in fundus images,” IEEE Transactions on Med. Imaging 32, 364–375 (2013).
[Crossref]

R. R. Bourne, G. A. Stevens, R. A. White, J. L. Smith, S. R. Flaxman, H. Price, J. B. Jonas, J. Keeffe, J. Leasher, and K. Naidoo, “Causes of vision loss worldwide, 1990–2010: a systematic analysis,” The Lancet Glob. Heal. 1, e339–e349 (2013).
[Crossref]

2012 (2)

H. Yazid, H. Arof, and H. M. Isa, “Automated identification of exudates and optic disc based on inverse surface thresholding,” J. Med. Syst. 36, 1997–2004 (2012).
[Crossref]

L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y. Li, S. Garg, K. W. Tobin Jr, and E. Chaum, “Exudate-based diabetic macular edema detection in fundus images using publicly available datasets,” Med. Image Analysis 16, 216–226 (2012).
[Crossref]

2010 (1)

D. Welfer, J. Scharcanski, and D. R. Marinho, “A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images,” computerized Med. Imaging Graph. 34, 228–235 (2010).
[Crossref]

2008 (2)

A. Sopharak, B. Uyyanonvara, S. Barman, and T. H. Williamson, “Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods,” Comput. Med. Imaging Graph. 32, 720–727 (2008).
[Crossref] [PubMed]

C. I. Sánchez, R. Hornero, M. I. López, M. Aboy, J. Poza, and D. Abásolo, “A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis,” Med. Eng. Phys. 30, 350–357 (2008).
[Crossref]

2007 (2)

M. Niemeijer, B. van Ginneken, S. R. Russell, M. S. Suttorp-Schulten, and M. D. Abramoff, “Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis,” Investig. Ophthalmol. Vis. Sci. 48, 2260–2267 (2007).
[Crossref]

A. D. Fleming, S. Philip, K. A. Goatman, G. J. Williams, J. A. Olson, and P. F. Sharp, “Automated detection of exudates for diabetic retinopathy screening,” Phys. Medicine Biol. 52, 7385 (2007).
[Crossref]

2006 (2)

C. Wolf and J.-M. Jolion, “Object count/area graphs for the evaluation of object detection and segmentation algorithms,” Int. J. Document Analysis Recognit. (IJDAR) 8, 280–296 (2006).
[Crossref]

C. D. Mathers and D. Loncar, “Projections of global mortality and burden of disease from 2002 to 2030,” PLoS Medicine 3, e442 (2006).
[Crossref] [PubMed]

2004 (2)

H. Li and O. Chutatape, “Automated feature extraction in color retinal images by a model based approach,” IEEE Transactions on Biomed. engineering 51, 246–254 (2004).
[Crossref]

D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Medicine 21, 84–90 (2004).
[Crossref]

2003 (1)

R. J. Tapp, J. E. Shaw, C. A. Harper, M. P. De Courten, B. Balkau, D. J. McCarty, H. R. Taylor, T. A. Welborn, and P. Z. Zimmet, “The prevalence of and factors associated with diabetic retinopathy in the australian population,” Diabetes Care 26, 1731–1737 (2003).
[Crossref] [PubMed]

2002 (3)

T. Walter, J.-C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina,” IEEE Transactions on Med. Imaging 21, 1236–1243 (2002).
[Crossref]

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: synthetic minority over-sampling technique,” J. Artif. Intell. Res. 16, 321–357 (2002).
[Crossref]

C. Sinthanayothin, J. F. Boyce, T. H. Williamson, H. L. Cook, E. Mensah, S. Lal, and D. Usher, “Automated detection of diabetic retinopathy on digital fundus images,” Diabet. Medicine 19, 105–112 (2002).
[Crossref]

2000 (1)

B. M. Ege, O. K. Hejlesen, O. V. Larsen, K. Møller, B. Jennings, D. Kerr, and D. A. Cavan, “Screening for diabetic retinopathy using computer based image analysis and statistical classification,” Comput. Methods Programs Biomed. 62, 165–175 (2000).
[Crossref] [PubMed]

1999 (1)

C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol. 83, 902–910 (1999).
[Crossref] [PubMed]

1996 (1)

L. Breiman, “Bagging predictors,” Mach. Learn. 24, 123–140 (1996).
[Crossref]

1993 (1)

R. Phillips, J. Forrester, and P. Sharp, “Automated detection and quantification of retinal exudates,” Graefe’s Arch. for Clin. Exp. Ophthalmol. 231, 90–94 (1993).
[Crossref]

Abásolo, D.

C. I. Sánchez, R. Hornero, M. I. López, M. Aboy, J. Poza, and D. Abásolo, “A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis,” Med. Eng. Phys. 30, 350–357 (2008).
[Crossref]

Aboy, M.

C. I. Sánchez, R. Hornero, M. I. López, M. Aboy, J. Poza, and D. Abásolo, “A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis,” Med. Eng. Phys. 30, 350–357 (2008).
[Crossref]

Abramoff, M. D.

L. Tang, M. Niemeijer, J. M. Reinhardt, M. K. Garvin, and M. D. Abramoff, “Splat feature classification with application to retinal hemorrhage detection in fundus images,” IEEE Transactions on Med. Imaging 32, 364–375 (2013).
[Crossref]

M. Niemeijer, B. van Ginneken, S. R. Russell, M. S. Suttorp-Schulten, and M. D. Abramoff, “Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis,” Investig. Ophthalmol. Vis. Sci. 48, 2260–2267 (2007).
[Crossref]

Acharya, U. R.

J. H. Tan, H. Fujita, S. Sivaprasad, S. V. Bhandary, A. K. Rao, K. C. Chua, and U. R. Acharya, “Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network,” Inf. Sci. 420, 66–76 (2017).
[Crossref]

Adal, K. M.

S. Ali, D. Sidibé, K. M. Adal, L. Giancardo, E. Chaum, T. P. Karnowski, and F. Mériaudeau, “Statistical atlas based exudate segmentation,” Comput. Med. Imaging Graph. 37, 358–368 (2013).
[Crossref] [PubMed]

Adam, J. B.

D. Kinga and J. B. Adam, “A method for stochastic optimization,” in International Conference on Learning Representations (ICLR), vol. 5 (2015).

Agurto, C.

C. Agurto, V. Murray, H. Yu, J. Wigdahl, M. Pattichis, S. Nemeth, E. S. Barriga, and P. Soliz, “A multiscale optimization approach to detect exudates in the macula,” IEEE J. Of Biomed. Heal. Informatics 18, 1328–1336 (2014).
[Crossref]

Akram, M. U.

M. U. Akram, A. Tariq, S. A. Khan, and M. Y. Javed, “Automated detection of exudates and macula for grading of diabetic macular edema,” Comput. Methods Programs Biomed. 114, 141–152 (2014).
[Crossref] [PubMed]

Ali, S.

S. Ali, D. Sidibé, K. M. Adal, L. Giancardo, E. Chaum, T. P. Karnowski, and F. Mériaudeau, “Statistical atlas based exudate segmentation,” Comput. Med. Imaging Graph. 37, 358–368 (2013).
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F. Araujo, R. Veras, A. Macedo, and F. Medeiros, “Automatic detection of exudates in retinal images using neural network,” Dept Comput. Fed. Univ. Braz. (2013).

Walter, T.

T. Walter, J.-C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina,” IEEE Transactions on Med. Imaging 21, 1236–1243 (2002).
[Crossref]

Warde-Farley, D.

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems, (2014), pp. 2672–2680.

Welborn, T. A.

R. J. Tapp, J. E. Shaw, C. A. Harper, M. P. De Courten, B. Balkau, D. J. McCarty, H. R. Taylor, T. A. Welborn, and P. Z. Zimmet, “The prevalence of and factors associated with diabetic retinopathy in the australian population,” Diabetes Care 26, 1731–1737 (2003).
[Crossref] [PubMed]

Welfer, D.

D. Welfer, J. Scharcanski, and D. R. Marinho, “A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images,” computerized Med. Imaging Graph. 34, 228–235 (2010).
[Crossref]

White, R. A.

R. R. Bourne, G. A. Stevens, R. A. White, J. L. Smith, S. R. Flaxman, H. Price, J. B. Jonas, J. Keeffe, J. Leasher, and K. Naidoo, “Causes of vision loss worldwide, 1990–2010: a systematic analysis,” The Lancet Glob. Heal. 1, e339–e349 (2013).
[Crossref]

Wigdahl, J.

C. Agurto, V. Murray, H. Yu, J. Wigdahl, M. Pattichis, S. Nemeth, E. S. Barriga, and P. Soliz, “A multiscale optimization approach to detect exudates in the macula,” IEEE J. Of Biomed. Heal. Informatics 18, 1328–1336 (2014).
[Crossref]

Williams, G. J.

A. D. Fleming, S. Philip, K. A. Goatman, G. J. Williams, J. A. Olson, and P. F. Sharp, “Automated detection of exudates for diabetic retinopathy screening,” Phys. Medicine Biol. 52, 7385 (2007).
[Crossref]

Williamson, T. H.

A. Sopharak, B. Uyyanonvara, S. Barman, and T. H. Williamson, “Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods,” Comput. Med. Imaging Graph. 32, 720–727 (2008).
[Crossref] [PubMed]

D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Medicine 21, 84–90 (2004).
[Crossref]

C. Sinthanayothin, J. F. Boyce, T. H. Williamson, H. L. Cook, E. Mensah, S. Lal, and D. Usher, “Automated detection of diabetic retinopathy on digital fundus images,” Diabet. Medicine 19, 105–112 (2002).
[Crossref]

C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol. 83, 902–910 (1999).
[Crossref] [PubMed]

Wisaeng, K.

K. Wisaeng, N. Hiransakolwong, and E. Pothiruk, “Automatic detection of exudates in retinal images based on threshold moving average models,” Biophysics 60, 288–297 (2015).
[Crossref]

Wolf, C.

C. Wolf and J.-M. Jolion, “Object count/area graphs for the evaluation of object detection and segmentation algorithms,” Int. J. Document Analysis Recognit. (IJDAR) 8, 280–296 (2006).
[Crossref]

Xiang, X.

W. Zhu, X. Xiang, T. D. Tran, and X. Xie, “Adversarial deep structural networks for mammographic mass segmentation,” ArXiv Prepr. ArXiv:1612.05970 (2016).

Xie, X.

W. Zhu, X. Xiang, T. D. Tran, and X. Xie, “Adversarial deep structural networks for mammographic mass segmentation,” ArXiv Prepr. ArXiv:1612.05970 (2016).

Xu, B.

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems, (2014), pp. 2672–2680.

Xu, X.

Z. Feng, J. Yang, L. Yao, Y. Qiao, Q. Yu, and X. Xu, “Deep retinal image segmentation: A fcn-based architecture with short and long skip connections for retinal image segmentation,” in International Conference on Neural Information Processing, (Springer, 2017), pp. 713–722.

Yang, J.

Z. Feng, J. Yang, L. Yao, Y. Qiao, Q. Yu, and X. Xu, “Deep retinal image segmentation: A fcn-based architecture with short and long skip connections for retinal image segmentation,” in International Conference on Neural Information Processing, (Springer, 2017), pp. 713–722.

Yao, L.

Z. Feng, J. Yang, L. Yao, Y. Qiao, Q. Yu, and X. Xu, “Deep retinal image segmentation: A fcn-based architecture with short and long skip connections for retinal image segmentation,” in International Conference on Neural Information Processing, (Springer, 2017), pp. 713–722.

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H. Yazid, H. Arof, and H. M. Isa, “Automated identification of exudates and optic disc based on inverse surface thresholding,” J. Med. Syst. 36, 1997–2004 (2012).
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C. Agurto, V. Murray, H. Yu, J. Wigdahl, M. Pattichis, S. Nemeth, E. S. Barriga, and P. Soliz, “A multiscale optimization approach to detect exudates in the macula,” IEEE J. Of Biomed. Heal. Informatics 18, 1328–1336 (2014).
[Crossref]

Yu, Q.

Z. Feng, J. Yang, L. Yao, Y. Qiao, Q. Yu, and X. Xu, “Deep retinal image segmentation: A fcn-based architecture with short and long skip connections for retinal image segmentation,” in International Conference on Neural Information Processing, (Springer, 2017), pp. 713–722.

Zahid, S.

M. M. Fraz, W. Jahangir, S. Zahid, M. M. Hamayun, and S. A. Barman, “Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification,” Biomed. Signal Process. Control. 35, 50–62 (2017).
[Crossref]

Zaremba, W.

T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” in Advances in Neural Information Processing Systems, (2016), pp. 2234–2242.

Zhang, X.

E. Decencière, X. Zhang, G. Cazuguel, B. Laÿ, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, and et al., “Feedback on a publicly distributed image database: the messidor database,” Image Analysis Stereol. 33, 231–234 (2014).
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X. Zhang, G. Thibault, E. Decencière, B. Marcotegui, B. Laÿ, R. Danno, G. Cazuguel, G. Quellec, M. Lamard, P. Massin, and et al., “Exudate detection in color retinal images for mass screening of diabetic retinopathy,” Med. Image Analysis 18, 1026–1043 (2014).
[Crossref]

Zhou, T.

P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” ArXiv Prepr. (2017).

Zhu, J.-Y.

P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” ArXiv Prepr. (2017).

Zhu, W.

W. Zhu, X. Xiang, T. D. Tran, and X. Xie, “Adversarial deep structural networks for mammographic mass segmentation,” ArXiv Prepr. ArXiv:1612.05970 (2016).

Zimmet, P. Z.

R. J. Tapp, J. E. Shaw, C. A. Harper, M. P. De Courten, B. Balkau, D. J. McCarty, H. R. Taylor, T. A. Welborn, and P. Z. Zimmet, “The prevalence of and factors associated with diabetic retinopathy in the australian population,” Diabetes Care 26, 1731–1737 (2003).
[Crossref] [PubMed]

Biomed. Signal Process. Control. (1)

M. M. Fraz, W. Jahangir, S. Zahid, M. M. Hamayun, and S. A. Barman, “Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification,” Biomed. Signal Process. Control. 35, 50–62 (2017).
[Crossref]

Biophysics (1)

K. Wisaeng, N. Hiransakolwong, and E. Pothiruk, “Automatic detection of exudates in retinal images based on threshold moving average models,” Biophysics 60, 288–297 (2015).
[Crossref]

Br. J. Ophthalmol. (1)

C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol. 83, 902–910 (1999).
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B. Harangi and A. Hajdu, “Automatic exudate detection by fusing multiple active contours and regionwise classification,” Comput. Biol. Medicine 54, 156–171 (2014).
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D. Welfer, J. Scharcanski, and D. R. Marinho, “A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images,” computerized Med. Imaging Graph. 34, 228–235 (2010).
[Crossref]

Diabet. Medicine (2)

D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabet. Medicine 21, 84–90 (2004).
[Crossref]

C. Sinthanayothin, J. F. Boyce, T. H. Williamson, H. L. Cook, E. Mensah, S. Lal, and D. Usher, “Automated detection of diabetic retinopathy on digital fundus images,” Diabet. Medicine 19, 105–112 (2002).
[Crossref]

Diabetes Care (1)

R. J. Tapp, J. E. Shaw, C. A. Harper, M. P. De Courten, B. Balkau, D. J. McCarty, H. R. Taylor, T. A. Welborn, and P. Z. Zimmet, “The prevalence of and factors associated with diabetic retinopathy in the australian population,” Diabetes Care 26, 1731–1737 (2003).
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G. Douzas and F. Bacao, “Effective data generation for imbalanced learning using conditional generative adversarial networks,” Expert. Syst. with Appl. 91, 464–471 (2018).
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IEEE J. Of Biomed. Heal. Informatics (1)

C. Agurto, V. Murray, H. Yu, J. Wigdahl, M. Pattichis, S. Nemeth, E. S. Barriga, and P. Soliz, “A multiscale optimization approach to detect exudates in the macula,” IEEE J. Of Biomed. Heal. Informatics 18, 1328–1336 (2014).
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H. Li and O. Chutatape, “Automated feature extraction in color retinal images by a model based approach,” IEEE Transactions on Biomed. engineering 51, 246–254 (2004).
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IEEE Transactions on Med. Imaging (2)

T. Walter, J.-C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina,” IEEE Transactions on Med. Imaging 21, 1236–1243 (2002).
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L. Tang, M. Niemeijer, J. M. Reinhardt, M. K. Garvin, and M. D. Abramoff, “Splat feature classification with application to retinal hemorrhage detection in fundus images,” IEEE Transactions on Med. Imaging 32, 364–375 (2013).
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Image Analysis Stereol. (1)

E. Decencière, X. Zhang, G. Cazuguel, B. Laÿ, B. Cochener, C. Trone, P. Gain, R. Ordonez, P. Massin, A. Erginay, and et al., “Feedback on a publicly distributed image database: the messidor database,” Image Analysis Stereol. 33, 231–234 (2014).
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C. Wolf and J.-M. Jolion, “Object count/area graphs for the evaluation of object detection and segmentation algorithms,” Int. J. Document Analysis Recognit. (IJDAR) 8, 280–296 (2006).
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M. Niemeijer, B. van Ginneken, S. R. Russell, M. S. Suttorp-Schulten, and M. D. Abramoff, “Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis,” Investig. Ophthalmol. Vis. Sci. 48, 2260–2267 (2007).
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Figures (5)

Fig. 1
Fig. 1 Retinal image with exudates.
Fig. 2
Fig. 2 (a) Structure of the U-net; (b) A building block of the MU-net(MU-net block); (c) MU-net consisting of 4 building blocks with different filter sizes.
Fig. 3
Fig. 3 GAN+MU-net. The cGAN network was first trained to generate label preserving synthetic images (with details of the training process described in the main context). The synthetic image pair together with the real image pair from the training dataset were used to train the GAN+MU-net.
Fig. 4
Fig. 4 Synthetic images generated using cGAN. (a) and (b) were the labeled binary images with the corresponding synthetic images (c) and (d) respectively. (c) was a good synthetic image that was similar to the real image in the sense of general looking and appearance of exudates, while (d) showed some artifacts. The inset in (d) was the enlarged version of the white rectangle area. The green spots were due to the falsely generated colors in the RGB channels.
Fig. 5
Fig. 5 (a) ROC curves of the U-net, MU-net block, MU-net and GAN+MU-net. (b) Precision-Recall curves of the four networks. GAN+MU-net had the highest AUCs as calculated either with the ROC curves or the Precision-Recall curves.

Tables (8)

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Table 1 U-net block Configuration

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Table 2 MU-net block Configuration

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Table 3 Definitions of the evaluation metrics

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Table 4 Evaluation of the performance of U-net, MU-net block, MU-net and GAN+MU-net at the lesion level. It was evaluated using the 22 test images (12 images with and 10 without exudates) in the e_ophtha_EX dataset.

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Table 5 Evaluation of the performance of the U-net, MU-net block, MU-net and GAN+MU-net at the image level using the 22 test images in the e_ophtha_EX dataset.

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Table 6 Comparison of the performances at the lesion level of the MU-net and GAN+MU-net using different testing datasets to test the generalization property of the networks.

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Table 7 Comparison of the performances of MU-net and GAN+MU-net on different testing datasets evaluated at the image level.

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Table 8 Comparison of the performances of our method with previous published methods using the four public datasets

Equations (3)

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

G * = arg min G max D cGAN ( G , D ) + λ L 1 ( G )
cGAN ( G , D ) = 𝔼 ( x , y ) ~ p data ( x , y ) [ log D ( x , y ) ] + 𝔼 x ~ p data ( x ) , z ~ p z ( z ) [ log ( 1 D ( x , G ( x , z ) ) ) ]
L 1 ( G ) = 𝔼 ( x , y ) ~ p data ( x , y ) , ( z ) ~ p z ( z ) [ y G ( x , z ) 1 ]

Metrics