Test-time Data Augmentation for Estimation of Heteroscedastic Aleatoric Uncertainty in Deep Neural Networks

Deep neural networks (DNNs) have revolutionized medical image analysis and disease diagnosis. Despite their impressive increase in performance, it is difficult to generate well-calibrated probabilistic outputs for such networks such that state-of-the-art networks fail to provide reliable uncertainty estimates regarding their decisions. We propose a simple but effective method using traditional data augmentation methods such as geometric and color transformations at test time. This allows to examine how much the network output varies in the vicinity of examples in the input spaces. Despite its simplicity, our method yields useful estimates for the input-dependent predictive uncertainties of deep neural networks. We showcase the impact of our method via the well-known collection of fundus images obtained from a previous Kaggle competition.