Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge

Aubreville M, Stathonikos N, Donovan TA, Klopfleisch R, Ammeling J, Ganz J, Wilm F, Veta M, Jabari S, Eckstein M, Annuscheit J, Krumnow C, Bozaba E, Çayır S, Gu H, Chen X, Jahanifar M, Shephard A, Kondo S, Kasai S, Kotte S, Saipradeep VG, Lafarge MW, Koelzer VH, Wang Z, Zhang Y, Yang S, Wang X, Breininger K, Bertram CA (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Book Volume: 94

Article Number: 103155

DOI: 10.1016/j.media.2024.103155

Abstract

Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories. This observation motivated the inception of the 2022 challenge on MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains. Ground truth for mitotic figure detection was established in two ways: a three-expert majority vote and an independent, immunohistochemistry-assisted set of labels. This work represents an overview of the challenge tasks, the algorithmic strategies employed by the participants, and potential factors contributing to their success. With an F1 score of 0.764 for the top-performing team, we summarize that domain generalization across various tumor domains is possible with today's deep learning-based recognition pipelines. However, we also found that domain characteristics not present in the training set (feline as new species, spindle cell shape as new morphology and a new scanner) led to small but significant decreases in performance. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, with only minor changes in the order of participants in the ranking.

Authors with CRIS profile

Additional Organisation(s)

Involved external institutions

University of California Los Angeles (UCLA) United States (USA) (US) University of Warwick United Kingdom (GB) Muroran Institute of Technology (MuIT) / 室蘭工業大学 Japan (JP) Technische Hochschule Ingolstadt Germany (DE) Virasoft Corporation United States (USA) (US) Radboud University Nijmegen Medical Centre / Radboudumc of voluit Radboud Universitair Medisch Centrum (UMC) Netherlands (NL) Niigata University of Health and Welfare Japan (JP) Tata Consultancy Services (TCS) India (IN) Universitätsspital Zürich (USZ) Switzerland (CH) University Medical Centre Utrecht (UMC Utrecht) Netherlands (NL) Harbin Institute of Technology (HIT) / 哈工大 China (CN) Sichuan University (SCU) / 四川大学 China (CN) Stanford University United States (USA) (US) Animal Medical Center (AMC) United States (USA) (US) Freie Universität Berlin Germany (DE) Veterinärmedizinische Universität Wien (Vetmeduni Vienna) / University of Veterinary Medicine, Vienna Austria (AT) Hochschule für Technik und Wirtschaft Berlin (HTW) Germany (DE)

How to cite

APA:

Aubreville, M., Stathonikos, N., Donovan, T.A., Klopfleisch, R., Ammeling, J., Ganz, J.,... Bertram, C.A. (2024). Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge. Medical Image Analysis, 94. https://doi.org/10.1016/j.media.2024.103155

MLA:

Aubreville, Marc, et al. "Domain generalization across tumor types, laboratories, and species — Insights from the 2022 edition of the Mitosis Domain Generalization Challenge." Medical Image Analysis 94 (2024).

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