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Uncertainty Evaluation Metric for Brain Tumour Segmentation (2005.14262v1)

Published 28 May 2020 in eess.IV and cs.CV

Abstract: In this paper, we develop a metric 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 components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.

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Authors (4)
  1. Raghav Mehta (21 papers)
  2. Angelos Filos (20 papers)
  3. Yarin Gal (170 papers)
  4. Tal Arbel (41 papers)
Citations (16)