Comparative evaluation of uncertainty estimation and decomposition methods on liver segmentation

Vanja Sophie Cangalovic, Felix Thielke, Hans Meine

Abstract: Deep neural networks need to be able to indicate error likelihood via reliable estimates of their predictive uncertainty when used in high-risk scenarios, such as medical decision support. This work contributes a systematic overview of state-of-the-art approaches for decomposing predictive uncertainty into aleatoric and epistemic components, and a comprehensive comparison for Bayesian neural networks (BNNs) between mutual information decomposition and the explicit modelling of both uncertainty types via an additional loss-attenuating neuron.

External IDs:dblp:journals/cars/CangalovicTM24