用双眼眼底图像+多视角深度学习，精准识别中风与短暂性脑缺血。
Advanced Assessment of Stroke in Retinal Fundus Imaging with Deep Multi-view Learning
- 设计双视角眼底图像输入的端到端网络，融合双眼特征
- 在患者与健康人数据上达到0.84的AUC值，区分中风与TIA
- 首次实现基于眼底图像的中风与TIA联合检测，适合临床筛查
中风是全球主要致死致残原因，早期精准诊断至关重要。眼底影像可显示中风风险标志：静脉扩张、动脉变细和血管扭曲。相比其他影像技术，眼底成像操作简便、无创、快速且成本低。本研究提出多视角中风网络（MVS-Net），利用双眼眼底图像进行中风及短暂性脑缺血（TIA）检测。不同于以往研究，首次采用端到端深度学习框架，融合左右眼的视盘中心与黄斑中心视角信息，提取代表性特征并建模双侧关系。在包含中风、TIA患者及健康对照的多中心数据集上验证，该方法在中风与TIA检测任务中取得0.84的AUC分数。
原文摘要 · Abstract (English)
Stroke is globally a major cause of mortality and morbidity, and hence accurate and rapid diagnosis of stroke is valuable. Retinal fundus imaging reveals the known markers of elevated stroke risk in the eyes, which are retinal venular widening, arteriolar narrowing, and increased tortuosity. In contrast to other imaging techniques used for stroke diagnosis, the acquisition of fundus images is easy, non-invasive, fast, and inexpensive. Therefore, in this study, we propose a multi-view stroke network (MVS-Net) to detect stroke and transient ischemic attack (TIA) using retinal fundus images. Contrary to existing studies, our study proposes for the first time a solution to discriminate stroke and TIA with deep multi-view learning by proposing an end-to-end deep network, consisting of multi-view inputs of fundus images captured from both right and left eyes. Accordingly, the proposed MVS-Net defines representative features from fundus images of both eyes and determines the relation within their macula-centered and optic nerve head-centered views. Experiments performed on a dataset collected from stroke and TIA patients, in addition to healthy controls, show that the proposed framework achieves an AUC score of 0.84 for stroke and TIA detection.
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