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

Marc Aubreville; Nikolas Stathonikos; Taryn A. Donovan; Robert Klopfleisch; Jonas Ammeling; Jonathan Ganz; Frauke Wilm; Mitko Veta; Samir Jabari; Markus Eckstein; Jonas Annuscheit; Christian Krumnow; Engin Bozaba; Sercan Cayir; Hongyan Gu; Xiang Anthony Chen; Mostafa Jahanifar; Adam Shepard; Satoshi Kondo; Sathosi Kasai; Sujatha Kotte; V. G. Saipradeep; Maxime W. Lafarge; Viktor H. Koelzer; Ziyue Wang; Yongbing Zhang; Xiyue Wang; Katharina Breininger; Christof A. Bertram

doi:10.1016/j.media.2024.103155

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Title (eng)

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

Author

Marc Aubreville

Nikolas Stathonikos

Taryn A. Donovan

Robert Klopfleisch

Jonas Ammeling

Jonathan Ganz

Frauke Wilm

Mitko Veta

Samir Jabari

Markus Eckstein

Jonas Annuscheit

Christian Krumnow

Engin Bozaba

Sercan Cayir

Hongyan Gu

Xiang Anthony Chen

Mostafa Jahanifar

Adam Shepard

Satoshi Kondo

Sathosi Kasai

Sujatha Kotte

V. G. Saipradeep

Maxime W. Lafarge

Viktor H. Koelzer

Ziyue Wang

Yongbing Zhang

Xiyue Wang

Katharina Breininger

Christof A. Bertram

University of Veterinary Medicine Vienna

Abstract (eng)

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.

Keywords (eng)

Domain GeneralizationHistopathologyChallengeDeep LearningMitosis

Type (eng)

journal article

Language

[eng]

Persistent identifier

https://phaidra.vetmeduni.ac.at/o:4285

DOI

10.1016/j.media.2024.103155

Is in series

Title (eng)