Uncertainty categories in medical image segmentation: a study of source-related diversity. (arXiv:2203.00238v2 [cs.LG] UPDATED)

Measuring uncertainties in the output of a deep learning method is useful in
several ways, such as in assisting with interpretation of the outputs, helping
build confidence with end users, and for improving the training and performance
of the networks. Several different methods have been proposed to estimate
uncertainties, including those from epistemic (relating to the model used) and
aleatoric (relating to the data) sources using test-time dropout and
augmentation, respectively. Not only are these uncertainty sources different,
but they …

arxiv diversity image medical segmentation study uncertainty