Joint Optimization of CycleGAN and CNN Classifier for Detection and Localization of Retinal Pathologies on Color Fundus Photographs

Retinal related diseases are the leading cause of vision loss, and severe retinal lesion causes irreversible damage to vision. Therefore, the automatic methods for retinal diseases detection based on medical images is essential for timely treatment. Considering that manual diagnosis and analysis of medical images require a large number of qualified experts, deep learning can effectively diagnosis and locate critical biomarkers. In this paper, we present a novel model by jointly optimize the cycle generative adversarial network (CycleGAN) and the convolutional neural network (CNN) to detect retinal diseases and localize lesion areas with limited training data. The CycleGAN with cycle consistency can generate more realistic and reliable images. The discriminator and the generator achieve a local optimal solution in an adversarial manner, and the generator and the classifier are in a cooperative manner to distinguish the domain of input images. A novel res-guided sampling block is proposed by combining learnable residual features and pixel-adaptive convolutions. A res-guided U-Net is constructed as the generator by substituting the traditional convolution with the res-guided sampling blocks. Our model achieve superior classification and localization performance on LAG, Ichallenge-PM and Ichallenge-AMD datasets. With clear localization for lesion areas, the competitive results reveal great potentials of the joint optimization network. The source code is available at https://github.com/jizexuan/JointOptmization.