Learning to Fuse and Reconstruct Multi-View Graphs for Diabetic Retinopathy Grading

• URL: http://arxiv.org/abs/2602.21944v1
• Date: Wed, 25 Feb 2026 14:28:57 GMT
• Title: Learning to Fuse and Reconstruct Multi-View Graphs for Diabetic Retinopathy Grading
• Authors: Haoran Li, Yuxin Lin, Huan Wang, Xiaoling Luo, Qi Zhu, Jiahua Shi, Huaming Chen, Bo Du, Johan Barthelemy, Zongyan Xue, Jun Shen, Yong Xu,
• Abstract summary: Diabetic retinopathy (DR) is one of the leading causes of vision loss worldwide.<n>Recent clinical practices leverage multi-view fundus images for DR detection with a wide coverage of the field of view.<n>We present MVGFDR, an end-to-end Multi-View Graph Fusion framework for DR grading.
• Score: 45.02913606252357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diabetic retinopathy (DR) is one of the leading causes of vision loss worldwide, making early and accurate DR grading critical for timely intervention. Recent clinical practices leverage multi-view fundus images for DR detection with a wide coverage of the field of view (FOV), motivating deep learning methods to explore the potential of multi-view learning for DR grading. However, existing methods often overlook the inter-view correlations when fusing multi-view fundus images, failing to fully exploit the inherent consistency across views originating from the same patient. In this work, we present MVGFDR, an end-to-end Multi-View Graph Fusion framework for DR grading. Different from existing methods that directly fuse visual features from multiple views, MVGFDR is equipped with a novel Multi-View Graph Fusion (MVGF) module to explicitly disentangle the shared and view-specific visual features. Specifically, MVGF comprises three key components: (1) Multi-view Graph Initialization, which constructs visual graphs via residual-guided connections and employs Discrete Cosine Transform (DCT) coefficients as frequency-domain anchors; (2) Multi-view Graph Fusion, which integrates selective nodes across multi-view graphs based on frequency-domain relevance to capture complementary view-specific information; and (3) Masked Cross-view Reconstruction, which leverages masked reconstruction of shared information across views to facilitate view-invariant representation learning. Extensive experimental results on MFIDDR, by far the largest multi-view fundus image dataset, demonstrate the superiority of our proposed approach over existing state-of-the-art approaches in diabetic retinopathy grading.

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