Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge

Ammeling J, Wilm F, Ganz J, Breininger K, Aubreville M (2023)

Publication Type: Conference contribution

Publication year: 2023

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13597 LNCS

Pages Range: 201-205

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Singapore, SGP

ISBN: 9783031336577

DOI: 10.1007/978-3-031-33658-4_19

Abstract

Robust mitosis detection on images from different tumor types, pathology labs, and species is a challenging task that was addressed in the MICCAI Mitosis Domain Generalization (MIDOG) 2022 challenge. In this work, we describe three reference algorithms that were provided as a baseline for the challenge: A Mask-RCNN-based instance segmentation model trained on the MIDOG 2022 dataset, and two different versions of the domain-adversarial RetinaNet which already served as the baseline for MIDOG 2021 challenge, one trained on the MIDOG 2022 dataset and the other trained only on human breast carcinoma from MIDOG 2021. The domain-adversarial RetinaNet trained on the MIDOG 2022 dataset had the highest F 1 score of 0.7135 on the final test set. When trained on breast carcinoma only, the same network had a much lower F 1 score of 0.4719, indicating a significant domain shift between mitotic figure and tissue representation in different tumor types.

Authors with CRIS profile

Frauke Wilm Lehrstuhl für Informatik 5 (Mustererkennung) Katharina Breininger Juniorprofessur für Artificial Intelligence in Medical Imaging

Involved external institutions

Technische Hochschule Ingolstadt DE Germany (DE)

How to cite

APA:

Ammeling, J., Wilm, F., Ganz, J., Breininger, K., & Aubreville, M. (2023). Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge. In Bin Sheng, Marc Aubreville (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 201-205). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.

MLA:

Ammeling, Jonas, et al. "Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge." Proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention , MICCAI 2022, Singapore, SGP Ed. Bin Sheng, Marc Aubreville, Springer Science and Business Media Deutschland GmbH, 2023. 201-205.

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