Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge - Ammeling, Jonas; Wilm, Frauke; Ganz, Jonathan; Breininger, Katharina; Aubreville, Marc (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
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.
CRIS-Team Scopus Importer:2023-10-20