# Classifying Mitotic Figures in the MIDOG25 Challenge with Deep Ensemble Learning and Rule Based Refinement

arXiv: https://arxiv.org/abs/2508.20919 · HTML: https://mlsanity.com/paper/2508.20919/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Nuclei instance segmentation and classification in histopathology images with StarDist](https://mlsanity.com/paper/2203.02284/) (2022)
- [Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)](https://mlsanity.com/paper/2501.04467/) (2025)
- [Standardized Medical Image Classification across Medical Disciplines](https://mlsanity.com/paper/2210.11091/) (2022)

## Abstract (arXiv)

Mitotic figures (MFs) are relevant biomarkers in tumor grading. Differentiating atypical MFs (AMFs) from normal MFs (NMFs) remains difficult, as manual annotation is time-consuming and subjective. In this work an ensemble of ConvNeXtBase models was trained with AUCMEDI and extend with a rule-based refinement (RBR) module. On the MIDOG25 preliminary test set, the ensemble achieved a balanced accuracy of 84.02%. While the RBR increased specificity, it reduced sensitivity and overall performance. The results show that deep ensembles perform well for AMF classification. RBR can increase specific metrics but requires further research.