Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challenge

Fuente: arXiv

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Detalles Bibliográficos
Autores principales:	Shen, Zhuoyan, Bär, Esther, Hawkins, Maria, Bräutigam, Konstantin, Collins-Fekete, Charles-Antoine
Formato:	Preprint
Publicado:	2025
Materias:	Image and Video Processing Computer Vision and Pattern Recognition
Acceso en línea:	Acceder al recurso
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author	Shen, Zhuoyan Bär, Esther Hawkins, Maria Bräutigam, Konstantin Collins-Fekete, Charles-Antoine
author_facet	Shen, Zhuoyan Bär, Esther Hawkins, Maria Bräutigam, Konstantin Collins-Fekete, Charles-Antoine
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2509_02585
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challenge Shen, Zhuoyan Bär, Esther Hawkins, Maria Bräutigam, Konstantin Collins-Fekete, Charles-Antoine Image and Video Processing Computer Vision and Pattern Recognition 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.
title	Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challenge
topic	Image and Video Processing Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2509.02585

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