发表机构
VTT Technical Research Centre of Finland（芬兰国家技术研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出BViT模型，利用双眼多视角视网膜眼底成像检测卒中，在自制Stroke-Data数据集上AUC达0.75，性能优于常规视觉Transformer。
AI 中文摘要
卒中仍是全球致死和发病的主要原因，凸显了对其进行准确且即时评估的重要性。视网膜眼底成像已成为卒中评估的有前景模态，因为视网膜可反映脑血管及神经危险因素。与传统神经成像技术不同，视网膜眼底成像为快速筛查提供了一种非侵入性、成本效益高且便携的替代方案。本研究探讨了利用双眼采集的以黄斑为中心和以视神经乳头为中心的视角进行视网膜眼底成像在卒中及短暂性脑缺血发作（TIA）检测中的可行性。据我们所知，本研究引入了首个用于卒中评估的视网膜眼底成像视觉Transformer模型，为捕捉视网膜模式提供了新方法。为此，我们提出了编织视觉Transformer（Braided Vision Transformer，BViT）模型，该模型从给定的多视角图像中提取代表性特征，同时捕捉双眼间的视角关系，从而更充分地理解与脑血管事件相关的视网膜生物标志物。在我们收集的Stroke-Data数据集上开展的实验表明，BViT在卒中检测中取得了0.75的AUC值，优于常规视觉Transformer。
英文摘要
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.
