标题： 基于Mamba的领域转移场景中的有丝分裂检测 显示英文标题

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

摘要： 有丝分裂检测在组织病理学图像中对于肿瘤评估起着关键作用。虽然可以利用机器学习算法来帮助医生准确执行此类任务，但这些算法在评估来自与训练数据不同的领域的图像时，性能会显著下降。在本工作中，我们提出了一种基于Mamba的方法，用于在领域偏移下的有丝分裂检测，该方法受到Mamba在医学影像分割任务中表现出的优异性能的启发。具体而言，我们的方法利用了VM-UNet架构来执行所讨论的任务，以及染色增强操作以进一步提高模型对领域偏移的鲁棒性。我们的方法已提交至MItosis DOmain Generalization（MIDOG）挑战赛的跟踪1。在MIDOG++数据集上进行的初步实验表明，所提出的方法还有很大的改进空间。 显示英文摘要

摘要： 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.