Vision for Vision — Deep Learning in Retinal Image Analysis

摘要

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Automated, fast and large-scale computer-aided diagnosis of medical images has become reality. The greatest breakthrough is Deep Learning. It already has huge impact in self-driving cars, industrial product inspection, surveillance, robotics and translation services, and in the medical arena it is outperforming human experts already in many domains. However, it is still largely a black box. What can we learn from recent insights in the functionality, nanometer-scale connectivity and self-organization of the human visual brain? We will discuss several recent breakthroughs in our understanding of visual perception and visual deep learning. We apply these techniques in the RetinaCheck project, a large screening / early warning project for eye damage due to diabetes. In China now an alarming 11.6% of the population has developed diabetes, due to genetic factors and fast lifestyle changes. In this project large amounts of retinal fundus images are acquired, and the e-cloud deep learning system successfully learns to identify early biomarkers of retinal disease. The circle is round: we can prevent blindness by learning from the visual system: vision for vision.