Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection
Journal:
arXiv
Published Date:
Mar 25, 2025
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.
