CRA-Net: Transformer guided category-relation attention network for diabetic retinopathy grading

Automated grading of diabetic retinopathy (DR) is an important means for assisting clinical diagnosis and preventing further retinal damage. However, imbalances and similarities between categories in the DR dataset make it highly challenging to accurately grade the severity of the condition. Furthermore, DR images encompass various lesions, and the pathological relationship information among these lesions can be easily overlooked. For instance, under different severity levels, the varying contributions of different lesions to accurate model grading differ significantly. To address the aforementioned issues, we design a transformer guided category-relation attention network (CRA-Net). Specifically, we propose a novel category attention block that enhances feature information within the class from the perspective of DR image categories, thereby alleviating class imbalance problems. Additionally, we design a lesion relation attention block that captures relationships between lesions by incorporating attention mechanisms in two primary aspects: capsule attention models the relative importance of different lesions, allowing the model to focus on more "informative" ones. Spatial attention captures the global position relationship between lesion features under transformer guidance, facilitating more accurate localization of lesions. Experimental and ablation studies on two datasets DDR and APTOS 2019 demonstrate the effectiveness of CRA-Net and obtain competitive performance.