Diabetic Retinopathy (DR), induced by diabetes, poses a significant risk of visual impairment. Accurate and effective grading of DR aids in the treatment of this condition. Yet existing models experience notable performance degradation on unseen domains due to domain shifts. Previous methods address this issue by simulating domain style through simple visual transformation and mitigating domain noise via learning robust representations. However, domain shifts encompass more than image styles. They overlook biases caused by implicit factors such as ethnicity, age, and diagnostic criteria. In our work, we propose a novel framework where representations of paired data from different domains are decoupled into semantic features and domain noise. The resulting augmented representation comprises original retinal semantics and domain noise from other domains, aiming to generate enhanced representations aligned with real-world clinical needs, incorporating rich information from diverse domains. Subsequently, to improve the robustness of the decoupled representations, class and domain prototypes are employed to interpolate the disentangled representations while data-aware weights are designed to focus on rare classes and domains. Finally, we devise a robust pixel-level semantic alignment loss to align retinal semantics decoupled from features, maintaining a balance between intra-class diversity and dense class features. Experimental results on multiple benchmarks demonstrate the effectiveness of our method on unseen domains. The code implementations are accessible on https://github.com/richard-peng-xia/DECO.

糖尿病视网膜病变（DR）是糖尿病引起的严重视力损害风险。我们提出了一个新颖的框架，其中来自不同领域的配对数据的表示被解耦为语义特征和领域噪声，以生成与临床需求对齐的增强表示，最终通过使用类和领域原型进行插值来改善解耦表示的鲁棒性，以及通过数据感知加权函数来关注罕见类别和领域。通过多个基准测试上的实验结果证明了我们方法在未知领域上的有效性。

糖尿病性视网膜病变中解缠表示的普遍化