A multi-label deep learning system for simultaneous detection of nine fundus conditions.
PURPOSE To develop and validate a deep learning model for simultaneous detection of nine fundus conditions from a single color fundus photograph. METHODS A development dataset of 236,242 color fundus images was assembled from 17 heterogeneous sources and partitioned at the patient level into training (70%), tuning (1...