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arXiv · 2411.00726

Cross-Fundus Transformer for Multi-modal Diabetic Retinopathy Grading with Cataract

Fan Xiao·Junlin Hou·Ruiwei Zhao·Rui Feng·Haidong Zou·Lina Lu·Yi Xu·Juzhao Zhang
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Abstract

Diabetic retinopathy (DR) is a leading cause of blindness worldwide and a common complication of diabetes. As two different imaging tools for DR grading, color fundus photography (CFP) and infrared fundus photography (IFP) are highly-correlated and complementary in clinical applications. To the best of our knowledge, this is the first study that explores a novel multi-modal deep learning framework to fuse the information from CFP and IFP towards more accurate DR grading. Specifically, we construct a dual-stream architecture Cross-Fundus Transformer (CFT) to fuse the ViT-based features of two fundus image modalities. In particular, a meticulously engineered Cross-Fundus Attention (CFA) module is introduced to capture the correspondence between CFP and IFP images. Moreover, we adopt both the single-modality and multi-modality supervisions to maximize the overall performance for DR grading. Extensive experiments on a clinical dataset consisting of 1,713 pairs of multi-modal fundus images demonstrate the superiority of our proposed method. Our code will be released for public access.

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Fan Xiao, Junlin Hou, Ruiwei Zhao, Rui Feng, Haidong Zou, Lina Lu, Yi Xu, Juzhao Zhang. 2024-11-01. Cross-Fundus Transformer for Multi-modal Diabetic Retinopathy Grading with Cataract. https://arxiv.org/abs/2411.00726

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