Uncertainty Evaluation Metric for Brain Tumour SegmentationDownload PDF

25 Jan 2020 (modified: 26 Jun 2020)MIDL 2020 Conference Blind SubmissionReaders: Everyone
  • 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
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