WMKA-Net: A Weighted Multi-Kernel Attention Network for Retinal Vessel Segmentation
This provides an efficient and precise solution for early screening of diabetic retinopathy, though it is incremental as it builds on existing segmentation methods.
The study tackled retinal vessel segmentation by proposing a dual-stage network with a reversible multi-scale fusion module and a vascular-oriented attention mechanism, achieving an accuracy of 0.9909, sensitivity of 0.9198, and specificity of 0.9953 on benchmark datasets.
Retinal vessel segmentation is crucial for intelligent ophthalmic diagnosis, yet it faces three major challenges: insufficient multi-scale feature fusion, disruption of contextual continuity, and noise interference. This study proposes a dual-stage solution to address these issues. The first stage employs a Reversible Multi-Scale Fusion Module (RMS) that uses hierarchical adaptive convolution to dynamically merge cross-scale features from capillaries to main vessels, self-adaptively calibrating feature biases. The second stage introduces a Vascular-Oriented Attention Mechanism, which models long-distance vascular continuity through an axial pathway and enhances the capture of topological key nodes, such as bifurcation points, via a dedicated bifurcation attention pathway. The synergistic operation of these two pathways effectively restores the continuity of vascular structures and improves the segmentation accuracy of complex vascular networks. Systematic experiments on the DRIVE, STARE, and CHASE-DB1 datasets demonstrate that WMKA-Net achieves an accuracy of 0.9909, sensitivity of 0.9198, and specificity of 0.9953, significantly outperforming existing methods. This model provides an efficient, precise, and robust intelligent solution for the early screening of diabetic retinopathy.