Attention-Driven Cascaded Network for Diabetic Retinopathy Grading from Fundus Images

Recently, academic and clinical communities have paid increasing attention to design computer-aided diagnosis methods for automatic and accurate diabetic retinopathy (DR) grading from fundus images. However, most existing methods either possess limited capability in extracting lesion-aware information or require manual lesion annotations, resulting in ordinary grading performance or additional undesirable labor. To address these issues, this paper proposes an end-to-end Attention-Driven Cascaded Network (ADCNet) for DR grading. Specifically, we first propose a hybrid attention module at the shallow layer by incorporating a multi-branch spatial attention and a loss-based attention to extract rich lesion-aware information without any manual lesion annotations. Then, we orderly cascade the lesion-aware information from shallow to high layers through an attention-driven aggregation strategy to obtain and integrate plentiful DR-related features. Finally, the grading score is generated by fusing DR-related features of all layers. Experimental results on two publicly available datasets demonstrate that the proposed ADCNet is competent for accurate DR grading, and outperforms the state-of-the-art methods on seven widely used evaluation criteria.