Lesion Learning Network with Relation Aware Transformer for Diabetic Retinopathy Grading

Diabetic Retinopathy (DR) is a leading cause of permanent blindness due to the difficulty of early screening. In this context, deep-learning-based automatic DR grading has the potential to significantly improve the diagnostic efficiency of ophthalmologists. However, accurate DR grading remains challenging due to intra-class variations and small lesions. To address this problem, a Lesion Learning Network with a Relation Aware Transformer (LLNet) is proposed in this paper to achieve precise DR grading. Specifically, a Lesion Information Extractor (LIE) is designed to recognize DR-related lesions and extract their fine-grained features through lesion annotations training or lesion-based contrastive learning. Then, the Lesion Saliency Transformer (LST) captures a discriminative lesion-salient sequence by interactively fusing LIE-extracted lesion features with self-attention features, thereby enhancing the perception of small lesions. Finally, the Lesion Relation Aware Transformer (LRAT) is proposed to establish an efficient relational model between DR lesion conditions and severity grades, thereby improving robustness to intra-class lesion variations and enabling more accurate grading predictions. Moreover, an adaptive lesion-learning strategy is designed for LLNet to conduct image-level learning and lesion-annotation-guided learning, thereby facilitating efficient utilization of DR data for generalization. Precision comparison experiments were conducted on the FGADR, DDR, and APTOS datasets. The proposed LLNet achieved accuracies of 82.7%, 86.1%, and 87.2%, respectively, surpassing comparative methods. Furthermore, the superior generalization capability of LLNet was validated by training on the DDR dataset and testing on the EyePACS dataset, achieving an accuracy of 72.1%.