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

arXiv: https://arxiv.org/abs/2509.02585 · HTML: https://mlsanity.com/paper/2509.02585/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Mitosis domain generalization in histopathology images -- The MIDOG challenge](https://mlsanity.com/paper/2204.03742/) (2022)
- [Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks](https://mlsanity.com/paper/1808.05896/) (2018)
- [OMG-Net: A Deep Learning Framework Deploying Segment Anything to Detect Pan-Cancer Mitotic Figures from Haematoxylin and Eosin-Stained Slides](https://mlsanity.com/paper/2407.12773/) (2024)

## Built on by (most cited first, 1 of 1)

- [Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge](https://mlsanity.com/paper/2606.07368/) (2026)

## Abstract (arXiv)

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"citesutton2019bitterlesson principle that emphasizes data scale over algorithmic novelty, we have publicly released two new datasets to bolster training data for both conventional citeShen2024framework and atypical mitoses citeshen202516780587. 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.