Uncertainty Estimation of Deep Learning Based method for Segmentation

Abstract

Deep Learning models have shown high performance in medical image analysis, specifically for retinal vessel segmentation. However, the current neural network models have not been able to represent the uncertainties that exist in images. The use of Bayesian neural networks has been shown across several studies to estimate the uncertainty of deep learning models. In this study, we briefly introduce uncertainty estimation and evaluate the predictive performance of some uncertainty estimation methods on retinal segmentation. We also show that using the estimates of the network in the loss function does not necessarily improve the segmentation performance. We end the talk by briefly discussing our current approach to get a reliable estimate of the uncertainty in this task.