Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challenge
This work addresses the problem of domain generalization in mitotic detection for cancer diagnosis, but it is incremental as it builds on existing principles and methods.
The authors tackled mitotic figure detection in histopathology for cancer prognostication by releasing new datasets and applying up-to-date training methods, achieving a Track-1 F1-Score of 0.8407 and a Track-2 balanced accuracy of 0.9107.
This report details our submission to the Mitotic Domain Generalization (MIDOG) 2025 challenge, which addresses the critical task of mitotic figure detection in histopathology for cancer prognostication. Following the "Bitter Lesson"\cite{sutton2019bitterlesson} principle that emphasizes data scale over algorithmic novelty, we have publicly released two new datasets to bolster training data for both conventional \cite{Shen2024framework} and atypical mitoses \cite{shen_2025_16780587}. Besides, we implement up-to-date training methodologies for both track and reach a Track-1 F1-Score of 0.8407 on our test set, as well as a Track-2 balanced accuracy of 0.9107 for atypical mitotic cell classification.