域偏移场景下的有丝分裂检测：一种基于Mamba的方法
Mitosis detection in domain shift scenarios: a Mamba-based approach
AI总结：
针对医学图像有丝分裂检测中域偏移导致性能下降的问题，提出基于Mamba、采用VM-UNet架构结合染色增强的检测方法，已提交MIDOG挑战赛，在MIDOG++数据集上实验显示仍有较大提升空间。
摘要：
组织病理学图像中的有丝分裂检测在肿瘤评估中发挥着关键作用。尽管机器学习算法可用于辅助医生准确完成这项任务，但当在与训练域不同的域的图像上进行评估时，这些算法的性能会出现显著下降。在本研究中，受Mamba在医学图像分割任务中展现出的优异性能启发，我们提出了一种基于Mamba的域偏移下有丝分裂检测方法。具体而言，该方法采用VM-UNet架构来执行所研究的任务，同时利用染色增强操作进一步提升模型应对域偏移的鲁棒性。我们的方法已提交至MItosis DOmain Generalization（MIDOG，有丝分裂域泛化）挑战赛的第一赛道。在MIDOG++数据集上开展的初步实验表明，所提出的方法仍有很大的提升空间。
英文摘要：
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.
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