Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection

• URL: http://arxiv.org/abs/2503.19329v1
• Date: Tue, 25 Mar 2025 03:44:57 GMT
• Title: Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection
• Authors: Yongting Hu, Yuxin Lin, Chengliang Liu, Xiaoling Luo, Xiaoyan Dou, Qihao Xu, Yong Xu,
• Abstract summary: We propose a novel method to overcome the challenges of difficult lesion information learning and inadequate multi-view fusion.<n> Specifically, we introduce a two-branch network to obtain both local lesion features and their global dependencies.<n>We present a cross-view fusion module to improve multi-view fusion and reduce redundancy.
• Score: 11.977013678444273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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