Feature Isolation for Hypothesis Testing in Retinal Imaging: An Ischemic Stroke Prediction Case Study

  • Gilbert Lim National University of Singapore
  • Zhan Wei Lim National University of Singapore
  • Dejiang Xu National University of Singapore
  • Daniel S.W. Ting Duke National University of Singapore Medical School
  • Tien Yin Wong Duke National University of Singapore Medical School
  • Mong Li Lee National University of Singapore
  • Wynne Hsu National University of Singapore

Abstract

Ischemic stroke is a leading cause of death and long-term disability that is difficult to predict reliably. Retinal fundus photography has been proposed for stroke risk assessment, due to its non-invasiveness and the similarity between retinal and cerebral microcirculations, with past studies claiming a correlation between venular caliber and stroke risk. However, it may be that other retinal features are more appropriate. In this paper, extensive experiments with deep learning on six retinal datasets are described. Feature isolation involving segmented vascular tree images is applied to establish the effectiveness of vessel caliber and shape alone for stroke classification, and dataset ablation is applied to investigate model generalizability on unseen sources. The results suggest that vessel caliber and shape could be indicative of ischemic stroke, and sourcespecific features could influence model performance.

Published
2019-07-17
Section
IAAI Technical Track: Emerging Papers