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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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