用小波分析和交叉注意力提升多视角糖尿病视网膜病变检测效果
Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection
- 双分支网络分别提取局部病灶特征与全局依赖关系
- 小波高频分量捕捉病灶边缘，结合语义信息增强难检病灶识别
- 跨视图融合模块减少冗余，适合医学影像多视角分析场景
多视角糖尿病视网膜病变（DR）检测近年来成为解决单视角病变不完整问题的有前景方法，但受限于病灶大小不一、分布零散，且现有方法在融合多视角信息时忽视其相关性与冗余性。为此，本文提出一种新方法，以克服病灶特征学习困难与多视角融合不足的问题。具体而言，设计了一个双分支网络，分别获取局部病灶特征与全局依赖关系。利用小波变换的高频分量挖掘病灶边缘信息，并通过全局语义增强，辅助难检病灶的学习。此外，提出一种跨视图融合模块，提升多视角融合效果并降低冗余。在多个公开大型数据集上的实验结果验证了该方法的有效性。代码已开源：https://github.com/HuYongting/WGLIN。
原文摘要 · Abstract (English)
Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single-view DR. However, it is still challenging due to the variable sizes and scattered locations of lesions. Furthermore, existing multi-view DR methods typically merge multiple views without considering the correlations and redundancies of lesion information across them. Therefore, we propose a novel method to overcome the challenges of difficult lesion information learning and inadequate multi-view fusion. Specifically, we introduce a two-branch network to obtain both local lesion features and their global dependencies. The high-frequency component of the wavelet transform is used to exploit lesion edge information, which is then enhanced by global semantic to facilitate difficult lesion learning. Additionally, we present a cross-view fusion module to improve multi-view fusion and reduce redundancy. Experimental results on large public datasets demonstrate the effectiveness of our method. The code is open sourced on https://github.com/HuYongting/WGLIN.
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