Uncertainty Evaluation Metric for Brain Tumour Segmentation

Raghav Mehta, Angelos Filos, Yarin Gal, Tal Arbel

Keywords: Brain Tumour Segmentation, Deep Neural Network, Uncertainty Evaluation

TL;DR: Developing a metric to evaluate uncertainties produced for the task of brain tumour segmentation

Track: short paper

Paper Type: methodological development

Abstract: In this paper, we describe and explore the metric that was designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the metrics explored based on a number of popular uncertainty measures evaluated on the BraTS2019 dataset

Presentation Upload Agreement: I agree that my presentation material (videos and slides) will be made public.