# Mitosis detection in domain shift scenarios: a Mamba-based approach

## TL;DR

This paper introduces a Mamba-based VM-UNet approach with stain augmentation to improve mitosis detection in histopathology images under domain shift conditions, addressing a key challenge in medical image analysis.

## Contribution

It proposes a novel Mamba-inspired method using VM-UNet and stain augmentation to enhance domain generalization in mitosis detection tasks.

## Key findings

- Preliminary results indicate significant potential for improvement.
- The approach was tested on the MIDOG++ dataset.
- Further development is needed for optimal performance.

## Abstract

Mitosis detection in histopathology images plays a key role in tumor assessment. Although machine learning algorithms could be exploited for aiding physicians in accurately performing such a task, these algorithms suffer from significative performance drop when evaluated on images coming from domains that are different from the training ones. In this work, we propose a Mamba-based approach for mitosis detection under domain shift, inspired by the promising performance demonstrated by Mamba in medical imaging segmentation tasks. Specifically, our approach exploits a VM-UNet architecture for carrying out the addressed task, as well as stain augmentation operations for further improving model robustness against domain shift. Our approach has been submitted to the track 1 of the MItosis DOmain Generalization (MIDOG) challenge. Preliminary experiments, conducted on the MIDOG++ dataset, show large room for improvement for the proposed method.