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.

翻译：糖尿病视网膜病变（DR）是糖尿病的常见并发症，也是全球致盲的主要原因之一。对其严重程度进行早期精准分级对疾病管理至关重要。尽管深度学习在自动DR分级领域展现出巨大潜力，但由于源域与目标域之间的分布偏移（即域泛化问题），其实际部署仍面临挑战。现有研究主要将性能下降归因于简单视觉差异导致的有限域偏移，但这种方法无法应对复杂的真实场景。相反，我们提出初步证据表明存在三个层面的泛化问题：视觉与退化风格偏移、诊断模式多样性以及数据不平衡。为解决这些问题，我们提出一个新颖的统一框架——泛化性糖尿病视网膜病变分级网络（GDRNet）。GDRNet包含三个关键组件：眼底视觉伪影增强模块（FundusAug）、动态混合监督损失函数（DahLoss）以及域类感知重平衡策略（DCR）。FundusAug通过视觉变换和图像退化生成逼真的增强图像，DahLoss则联合利用像素级一致性与图像级语义信息，捕获多样化的诊断模式并构建泛化特征表示。此外，DCR从域类视角缓解数据不平衡问题，避免对罕见域类对的过度强调。最终，我们设计了一个公开基准用于公平评估。与先进方法的广泛比较实验及详尽的消融研究表明，GDRNet具备卓越的有效性与泛化能力。