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Mitosis domain generalization in histopathology images - The MIDOG challenge.

In Medical image analysis

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 F1 score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task.

Aubreville Marc, Stathonikos Nikolas, Bertram Christof A, Klopfleisch Robert, Ter Hoeve Natalie, Ciompi Francesco, Wilm Frauke, Marzahl Christian, Donovan Taryn A, Maier Andreas, Breen Jack, Ravikumar Nishant, Chung Youjin, Park Jinah, Nateghi Ramin, Pourakpour Fattaneh, Fick Rutger H J, Ben Hadj Saima, Jahanifar Mostafa, Shephard Adam, Dexl Jakob, Wittenberg Thomas, Kondo Satoshi, Lafarge Maxime W, Koelzer Viktor H, Liang Jingtang, Wang Yubo, Long Xi, Liu Jingxin, Razavi Salar, Khademi April, Yang Sen, Wang Xiyue, Erber Ramona, Klang Andrea, Lipnik Karoline, Bolfa Pompei, Dark Michael J, Wasinger Gabriel, Veta Mitko, Breininger Katharina

2022-Nov-23

Challenge, Deep Learning, Domain generalization, Histopathology, Mitosis