  Diabetic retinopathy (DR) is a complication of diabetes that severely affects
eyes. It can be graded into five levels of severity according to international
protocol. However, optimizing a grading model to have strong generalizability
requires a large amount of balanced training data, which is difficult to
collect particularly for the high severity levels. Typical data augmentation
methods, including random flipping 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 and lesion segmentation model. The proposed retina generator
is conditioned on the structural and lesion masks, as well as adaptive grading
vectors sampled from the latent grading space, which can be adopted to control
the synthesized grading severity. Moreover, a multi-scale spatial and channel
attention module is devised to improve the generation ability to synthesize
details. 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, as well as the FGADR dataset, we
validate the effectiveness of our method, which can both synthesize highly
realistic (1280 x 1280) controllable fundus images and contribute to the DR
grading task.
