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

## TL;DR

This paper introduces a multi-task neural network designed to improve atypical mitosis recognition in histopathology images, especially under domain shift conditions, by leveraging auxiliary tasks to enhance focus on relevant features.

## Contribution

The paper proposes a novel multi-task learning approach that addresses domain shift in mitosis recognition by using auxiliary tasks to improve model robustness and focus.

## Key findings

- Promising performance on three distinct datasets
- Effective in reducing background influence under domain shift
- Applicable to histopathology image analysis

## Abstract

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.