Braided Vision Transformer for Stroke Detection in Multi-view Retinal Fundus Imaging

• URL: http://arxiv.org/abs/2608.14722v1
• Date: Wed, 12 Aug 2026 12:04:43 GMT
• Title: Braided Vision Transformer for Stroke Detection in Multi-view Retinal Fundus Imaging
• Abstract summary: Retinal fundus imaging has emerged as a promising modality for stroke assessment.<n>We propose the Braided Vision Transformer (BViT) model, which extracts representative features from the given multi-view images.<n>BViT achieves an AUC score of 0.75 for stroke detection, outperforming regular vision transformers.
• Score: 0.5156484100374058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stroke remains a leading cause of mortality and morbidity worldwide, emphasizing the importance of its accurate and immediate assessment. Retinal fundus imaging has emerged as a promising modality for stroke assessment, as the retina reflects cerebrovascular and neurological risk factors. Contrary to conventional neuroimaging techniques, retinal fundus imaging offers a non-invasive, cost-effective, and portable alternative for rapid screening. This paper explores the feasibility of retinal fundus imaging for stroke and transient ischemic attack (TIA) detection using macula-centric and optic nerve head-centric views captured from both eyes. Our study introduces, to the best of our knowledge, the first vision transformer model for retinal fundus imaging in stroke assessment, offering a novel approach for capturing retinal patterns. Thereby, we propose the Braided Vision Transformer (BViT) model, which extracts representative features from the given multi-view images while simultaneously capturing inter-view relationships across both eyes, enabling a more informative understanding of retinal biomarkers associated with cerebrovascular events. Experiments conducted on our collected Stroke-Data dataset demonstrate that BViT achieves an AUC score of 0.75 for stroke detection, outperforming regular vision transformers.

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