# A multi-task neural network for atypical mitosis recognition under domain shift

arXiv: https://arxiv.org/abs/2508.21035 · HTML: https://mlsanity.com/paper/2508.21035/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 28 Aug 2025.

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

- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images](https://mlsanity.com/paper/2005.14511/) (2020)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)

## Abstract (arXiv)

Recognizing atypical mitotic figures in histopathology images allows physicians to correctly assess tumor aggressiveness. Although machine learning models could be exploited for automatically performing such a task, under domain shift these models suffer from significative performance drops. In this work, an approach based on multi-task learning is proposed for addressing this problem. By exploiting auxiliary tasks, correlated to the main classification task, the proposed approach, submitted to the track 2 of the MItosis DOmain Generalization (MIDOG) challenge, aims to aid the model to focus only on the object to classify, ignoring the domain varying background of the image. The proposed approach shows promising performance in a preliminary evaluation conducted on three distinct datasets, i.e., the MIDOG 2025 Atypical Training Set, the Ami-Br dataset, as well as the preliminary test set of the MIDOG25 challenge.