Cross-Structure and Semantic Enhancement for Diabetic Retinopathy Grading

Challenges such as highly variable lesion appearances and complex structural distributions hinder model performance in diabetic retinopathy (DR) grading tasks. To address these issues, we focus on guiding the network toward discriminative feature representation by prioritizing diagnostically relevant information and modeling intricate dependencies between features and DR grades. Specifically, we propose a DR grading network that integrates the Kolmogorov-Arnold Network (KAN) into a Convolution-Vision Transformer (CNN-ViT) cooperative framework, where convolutions and Transformer encoders capture spatial patterns, hierarchical structures, and context, while KAN enhances non-linear semantic dependency modeling. Additionally, we introduce a Cross-Structure (CS) attention module to emphasize relevant features. The proposed modules form the Convolution-Cross-Structure-KAN (CCSK) block, which serves as the backbone of our network, CCSKFormer, enabling more accurate DR grading. The proposed model achieves outstanding performance on two public datasets, with comparisons and ablation studies further validating the effectiveness of the individual modules (https://github.com/xia-xx-cv/CCSKformer).