High-Resolution Diabetic Retinopathy Image Synthesis Manipulated by Grading and Lesions

Diabetic retinopathy (DR) is a complication of diabetes that severely affects eyes, and can be graded into five levels according to international protocol. However, optimizing a grading model with strong generalization ability requires large balanced training data, which is difficult to collect in general but particularly for the high severity levels. Typical data augmentation methods, including flip and rotation cannot generate data with high diversity. In this paper, we propose a diabetic retinopathy generative adversarial network (DR-GAN) to synthesize high-resolution fundus images, which can be manipulated with arbitrary grading and lesion information. Thus, large-scale generated data can be used for more meaningful augmentation to train a DR grading model. The proposed retina generator is conditioned on vessel and lesion masks, and adaptive grading vectors sampled from the latent grading space, which can be adopted to control the synthesized grading severity. Moreover, multi-scale discriminators are designed to operate from large to small receptive fields, and joint adversarial losses are adopted to optimize the whole network in an end-to-end manner. With extensive experiments evaluated on the EyePACS dataset connected to Kaggle, we validate the effectiveness of our method, which can both synthesize highly realistic (\(1280 \times 1280\)) controllable fundus images and contribute to the DR grading task.