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在肿瘤评估中起着关键作用。虽然可以利用机器学习算法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会显著性能下降。在这项工作中，我们提出了一种基于Mamba的方法，在领域转移下进行有丝分裂检测，这是受到Mamba在医学影像分割任务中表现出的有希望的性能的启发。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转移的鲁棒性。我们的方法已提交到MItosis DOmain Generalization（MIDOG）挑战赛的轨道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.