Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge
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| Formato: | Preprint |
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2023
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| author | Aubreville, Marc Stathonikos, Nikolas Donovan, Taryn A. Klopfleisch, Robert Ganz, Jonathan Ammeling, Jonas Wilm, Frauke Veta, Mitko Jabari, Samir Eckstein, Markus Annuscheit, Jonas Krumnow, Christian Bozaba, Engin Cayir, Sercan Gu, Hongyan Chen, Xiang 'Anthony' Jahanifar, Mostafa Shephard, Adam Kondo, Satoshi Kasai, Satoshi Kotte, Sujatha Saipradeep, VG Lafarge, Maxime W. Koelzer, Viktor H. Wang, Ziyue Zhang, Yongbing Yang, Sen Wang, Xiyue Breininger, Katharina Bertram, Christof A. |
| author_facet | Aubreville, Marc Stathonikos, Nikolas Donovan, Taryn A. Klopfleisch, Robert Ganz, Jonathan Ammeling, Jonas Wilm, Frauke Veta, Mitko Jabari, Samir Eckstein, Markus Annuscheit, Jonas Krumnow, Christian Bozaba, Engin Cayir, Sercan Gu, Hongyan Chen, Xiang 'Anthony' Jahanifar, Mostafa Shephard, Adam Kondo, Satoshi Kasai, Satoshi Kotte, Sujatha Saipradeep, VG Lafarge, Maxime W. Koelzer, Viktor H. Wang, Ziyue Zhang, Yongbing Yang, Sen Wang, Xiyue Breininger, Katharina Bertram, Christof A. |
| contents | 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 consensus 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 $F_1$ 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, but with only minor changes in the order of participants in the ranking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_15589 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge Aubreville, Marc Stathonikos, Nikolas Donovan, Taryn A. Klopfleisch, Robert Ganz, Jonathan Ammeling, Jonas Wilm, Frauke Veta, Mitko Jabari, Samir Eckstein, Markus Annuscheit, Jonas Krumnow, Christian Bozaba, Engin Cayir, Sercan Gu, Hongyan Chen, Xiang 'Anthony' Jahanifar, Mostafa Shephard, Adam Kondo, Satoshi Kasai, Satoshi Kotte, Sujatha Saipradeep, VG Lafarge, Maxime W. Koelzer, Viktor H. Wang, Ziyue Zhang, Yongbing Yang, Sen Wang, Xiyue Breininger, Katharina Bertram, Christof A. Computer Vision and Pattern Recognition 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 consensus 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 $F_1$ 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, but with only minor changes in the order of participants in the ranking. |
| title | Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.15589 |