Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning

1 code implementation • 26 Aug 2021 • Sora Iwamoto, Bisser Raytchev, Toru Tamaki, Kazufumi Kaneda

In this paper we propose a novel method which leverages the uncertainty measures provided by Bayesian deep networks through curriculum learning so that the uncertainty estimates are fed back to the system to resample the training data more densely in areas where uncertainty is high.

Segmentation Semantic Segmentation