Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 80, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2022.104370
Keywords
Diabetic retinopathy grading; Cascaded network; Attention; Fundus image; Deep learning
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This paper proposes an end-to-end Attention-Driven Cascaded Network (ADCNet) for automatic grading of diabetic retinopathy (DR) from retinal fundus images. It extracts lesion-aware information using a hybrid attention module and an attention-driven aggregation strategy, achieving accurate DR grading.
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.
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