提出新网络提升眼底血管分割精度，解决多尺度融合与连续性问题。
WMKA-Net: A Weighted Multi-Kernel Attention Network for Retinal Vessel Segmentation
- 分两阶段：动态融合多尺度特征，用轴向路径建模血管连续性
- 在三个数据集上达99.09%准确率，敏感度91.98%，优于现有方法
- 适合糖尿病视网膜病变早期筛查，对复杂血管结构分割效果好
眼底血管分割对智能眼科诊断至关重要，但面临多尺度特征融合不足、上下文连续性破坏和噪声干扰三大挑战。本文提出双阶段解决方案：第一阶段采用可逆多尺度融合模块（RMS），通过分层自适应卷积动态融合从毛细血管到主血管的跨尺度特征，并自适应校准特征偏差；第二阶段引入血管导向注意力机制，通过轴向路径建模远距离血管连续性，同时通过专用分叉点注意力路径增强对分叉等拓扑关键点的捕捉。两个路径协同作用有效恢复血管结构连续性，显著提升复杂血管网络分割精度。在DRIVE、STARE、CHASE-DB1三个数据集上的系统实验表明，WMKA-Net达到0.9909的准确率、0.9198的敏感度和0.9953的特异度，显著优于现有方法。该模型为糖尿病视网膜病变的早期筛查提供了高效、精准、鲁棒的智能解决方案。
原文摘要 · Abstract (English)
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.
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