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

• URL: http://arxiv.org/abs/2508.21033v1
• Date: Thu, 28 Aug 2025 17:38:30 GMT
• Title: Mitosis detection in domain shift scenarios: a Mamba-based approach
• Authors: Gennaro Percannella, Mattia Sarno, Francesco Tortorella, Mario Vento,
• Abstract summary: We propose a Mamba-based approach for mitosis detection under domain shift.<n>Specifically, our approach exploits a VM-UNet architecture for carrying out the addressed task.<n>Preliminary experiments, conducted on the MIDOG++ dataset, show large room for improvement.
• Score: 3.2490463485798338
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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