Low-Rank Adaptive Structural Priors for Generalizable Diabetic Retinopathy Grading
Journal:
arXiv
Published Date:
Apr 27, 2025
Abstract
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.
