  Diabetic Retinopathy (DR) is a common complication of diabetes and a leading
cause of blindness worldwide. Early and accurate grading of its severity is
crucial for disease management. Although deep learning has shown great
potential for automated DR grading, its real-world deployment is still
challenging due to distribution shifts among source and target domains, known
as the domain generalization problem. Existing works have mainly attributed the
performance degradation to limited domain shifts caused by simple visual
discrepancies, which cannot handle complex real-world scenarios. Instead, we
present preliminary evidence suggesting the existence of three-fold
generalization issues: visual and degradation style shifts, diagnostic pattern
diversity, and data imbalance. To tackle these issues, we propose a novel
unified framework named Generalizable Diabetic Retinopathy Grading Network
(GDRNet). GDRNet consists of three vital components: fundus visual-artifact
augmentation (FundusAug), dynamic hybrid-supervised loss (DahLoss), and
domain-class-aware re-balancing (DCR). FundusAug generates realistic augmented
images via visual transformation and image degradation, while DahLoss jointly
leverages pixel-level consistency and image-level semantics to capture the
diverse diagnostic patterns and build generalizable feature representations.
Moreover, DCR mitigates the data imbalance from a domain-class view and avoids
undesired over-emphasis on rare domain-class pairs. Finally, we design a
publicly available benchmark for fair evaluations. Extensive comparison
experiments against advanced methods and exhaustive ablation studies
demonstrate the effectiveness and generalization ability of GDRNet.
