用Mamba模型提升病理图像中分裂细胞检测的跨域适应能力
Mitosis detection in domain shift scenarios: a Mamba-based approach
- 基于VM-UNet架构结合Mamba机制，增强模型对染色差异的鲁棒性
- 在MIDOG++数据集上初步测试，性能仍有较大提升空间
- 适合关注医学图像跨域泛化与新型架构应用的研究者
病理图像中的有丝分裂检测对肿瘤评估至关重要。尽管机器学习算法可辅助医生提高检测精度，但在来自不同领域（如染色方式不同）的图像上，现有方法性能显著下降。本文提出一种基于Mamba的有丝分裂检测方法，受其在医学图像分割中表现优异的启发。采用VM-UNet架构完成任务，并引入染色增强策略以提升模型对域偏移的鲁棒性。该方法已提交至MItosis DOmain Generalization（MIDOG）挑战赛第1赛道。初步实验在MIDOG++数据集上进行，结果显示当前方法仍有较大改进空间。
原文摘要 · Abstract (English)
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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