Diabetic retinopathy (DR), a serious ocular complication of diabetes, is one of the primary causes of vision loss among retinal vascular diseases. Deep learning methods have been extensively applied in the grading of diabetic retinopathy (DR). However, their performance declines significantly when applied to data outside the training distribution due to domain shifts. Domain generalization (DG) has emerged as a solution to this challenge. However, most existing DG methods overlook lesion-specific features, resulting in insufficient accuracy. In this paper, we propose a novel approach that enhances existing DG methods by incorporating structural priors, inspired by the observation that DR grading is heavily dependent on vessel and lesion structures. We introduce Low-rank Adaptive Structural Priors (LoASP), a plug-and-play framework designed for seamless integration with existing DG models. LoASP improves generalization by learning adaptive structural representations that are finely tuned to the complexities of DR diagnosis. Extensive experiments on eight diverse datasets validate its effectiveness in both single-source and multi-source domain scenarios. Furthermore, visualizations reveal that the learned structural priors intuitively align with the intricate architecture of the vessels and lesions, providing compelling insights into their interpretability and diagnostic relevance.

本研究针对糖尿病性视网膜病(DR)分级中的领域泛化问题，提出了一种新颖的低秩自适应结构先验（LoASP）方法。该方法通过引入结构先验信息，显著提升了现有泛化方法在不同数据源上的表现，并使得模型更易于解释，为糖尿病性视网膜病的诊断提供了新的思路和方向。

可泛化糖尿病性视网膜病分级的低秩自适应结构先验