On the Uncertainty of Retinal Artery-Vein Classification with Dense Fully-Convolutional Neural Networks

Abstract

Retinal imaging is a valuable tool in diagnosing many eye diseases but offers opportunities to have a direct view to central nervous system and its blood vessels. The accurate measurement of the characteristics of retinal vessels allows not only analysis of retinal diseases but also many systemic diseases like diabetes and other cardiovascular or cerebrovascular diseases. This analysis benefits from precise blood vessel characterization. Automatic machine learning methods are typically trained in the supervised manner where a training set with ground truth data is available. Due to difficulties in precise pixelwise labeling, the question of the reliability of a trained model arises. This paper addresses this question using Bayesian deep learning and extends recent research on the uncertainty quantification of retinal vasculature and artery-vein classification. It is shown that state-of-the-art results can be achieved by using the trained model. An analysis of the predictions for cases where the class labels are unavailable is given.