Aubreville, M, Stathonikos, N, Bertram, CA et al. (38 more authors) (2023) Mitosis domain generalization in histopathology images — The MIDOG challenge. Medical Image Analysis, 84. 102699. ISSN 1361-8415
Abstract
The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an
score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier B.V. All rights reserved. This is an author-accepted version of an article published in Medical Image Analysis. |
Keywords: | Domain generalization; Histopathology; Challenge; Deep Learning; Mitosis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Jan 2023 14:25 |
Last Modified: | 23 Nov 2023 01:13 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.media.2022.102699 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194640 |