Uncertainty Categories in Medical Image Segmentation: A Study of Source-Related Diversity

Luke Whitbread, Mark Jenkinson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Measuring uncertainties in the output of a deep learning method is useful in several ways, such as in assisting with interpretation of the outputs, helping build confidence with end users, and for improving the training and performance of the networks. Several different methods have been proposed to estimate uncertainties, including those from epistemic (relating to the model used) and aleatoric (relating to the data) sources using test-time dropout and augmentation, respectively. Not only are these uncertainty sources different, but they are governed by parameter settings (e.g., dropout rate or type and level of augmentation) that establish even more distinct uncertainty categories. This work investigates how different the uncertainties are from these categories, for magnitude and spatial pattern, to empirically address the question of whether they provide usefully distinct information that should be captured whenever uncertainties are used. We take the well characterised BraTS challenge dataset to demonstrate that there are substantial differences in both magnitude and spatial pattern of uncertainties from the different categories, and discuss the implications of these in various use cases.

Original languageEnglish
Title of host publicationUncertainty for Safe Utilization of Machine Learning in Medical Imaging - 4th International Workshop, UNSURE 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsCarole H. Sudre, Carole H. Sudre, Christian F. Baumgartner, Adrian Dalca, Adrian Dalca, William M. Wells III, Chen Qin, Ryutaro Tanno, Koen Van Leemput, Koen Van Leemput, William M. Wells III
PublisherSpringer Science and Business Media Deutschland GmbH
Pages26-35
Number of pages10
ISBN (Print)9783031167485
DOIs
Publication statusPublished or Issued - 2022
Externally publishedYes
Event4th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 18 Sept 202218 Sept 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13563 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period18/09/2218/09/22

Keywords

  • Diversity
  • Reliability
  • Stability
  • Uncertainties

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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