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Evolutionary Generative Adversarial Networks applied to computer-assisted diabetic retinopathy diagnosis


Undiagnosed diabetic retinopathy (DR) leads to vision impairment and blindness, and its early detection can significantly reduce severe vision impairment by 50%. The presence of exudates in fundus images is one of the first signs of such a disease, but their manual detection is examiner-dependent and time-consuming. In this proposal, we aim at coping with the lack of data concerning computer-assisted DR detection by synthesizing retina images through Generative Adversarial Networks (GANs). The proposal will introduce the concept of Evolving GANs, where evolutionary optimization will be employed to either fine-tune GAN hyperparameters and to learn composite loss functions. The Brazilian team will be in charge of the machine learning side; meanwhile the Australian team will take care of the dataset and the knowledge about the problem. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
OLIVEIRA, GUILHERME C.; NGO, QUOC C.; PASSOS, LEANDRO A.; PAPA, JOAO P.; JODAS, DANILO S.; KUMAR, DINESH. Tabular data augmentation for video-based detection of hypomimia in Parkinson's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 240, p. 8-pg., . (19/00585-5, 18/15597-6, 14/12236-1, 19/07665-4, 13/07375-0, 19/02205-5)

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