面向半监督医学图像分割的多样性增强协作Mamba
Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
AI总结：
本文提出DCMamba框架，从数据、网络和特征三个视角增强多样性，用于半监督医学图像分割，并在Synapse数据集上显著超越现有方法。
摘要：
获取高质量的医学图像分割标注数据既繁琐又昂贵。半监督分割技术通过利用无标签数据生成伪标签来减轻这一负担。近年来，以Mamba为代表的先进状态空间模型已展现出高效处理长程依赖的能力，这促使我们探索其在半监督医学图像分割中的潜力。本文提出了一种新颖的多样性增强协作Mamba框架（即DCMamba），用于半监督医学图像分割，该框架从数据、网络和特征三个视角探索并利用多样性。首先，从数据视角出发，我们结合Mamba的扫描建模特性，开发了块级弱-强混合增强方法。其次，从网络视角出发，我们引入了一个多样扫描协作模块，该模块能够受益于不同扫描方向产生的预测差异。此外，从特征视角出发，我们采用不确定性加权对比学习机制来增强特征表示的多样性。实验表明，我们的DCMamba显著优于其他半监督医学图像分割方法，例如，在Synapse数据集上使用20%标注数据时，比最新的基于SSM的方法提升了6.69%。
英文摘要：
Acquiring high-quality annotated data for medical image segmentation is tedious and costly. Semi-supervised segmentation techniques alleviate this burden by leveraging unlabeled data to generate pseudo labels. Recently, advanced state space models, represented by Mamba, have shown efficient handling of long-range dependencies. This drives us to explore their potential in semi-supervised medical image segmentation. In this paper, we propose a novel Diversity-enhanced Collaborative Mamba framework (namely DCMamba) for semi-supervised medical image segmentation, which explores and utilizes the diversity from data, network, and feature perspectives. Firstly, from the data perspective, we develop patch-level weak-strong mixing augmentation with Mamba's scanning modeling characteristics. Moreover, from the network perspective, we introduce a diverse-scan collaboration module, which could benefit from the prediction discrepancies arising from different scanning directions. Furthermore, from the feature perspective, we adopt an uncertainty-weighted contrastive learning mechanism to enhance the diversity of feature representation. Experiments demonstrate that our DCMamba significantly outperforms other semi-supervised medical image segmentation methods, e.g., yielding the latest SSM-based method by 6.69% on the Synapse dataset with 20% labeled data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
