Advanced Assessment of Stroke in Retinal Fundus Imaging With Deep Multi-View Learning.

Stroke is globally a major cause of mortality and morbidity, and hence, accurate risk assessment and diagnosis of stroke are 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 assessment, the acquisition of fundus images is easy, non-invasive, fast, and inexpensive. This paper examines the feasibility of utilizing retinal fundus imaging to differentiate individuals with stroke or transient ischemic attack (TIA), aiming to assess its potential for screening or diagnostic applications. Therefore, in this study, we propose a multi-view stroke network (MVS-Net) to detect stroke and 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.