Semi-Mamba-UNet：用于半监督医学图像分割的像素级对比与像素级交叉监督的基于视觉Mamba的UNet
Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation
AI总结：
针对医学图像分割中长距离依赖处理效率低和标注成本高的问题，提出Semi-Mamba-UNet半监督框架，结合视觉Mamba与CNN的UNet进行像素级交叉监督和对比学习，显著提升了分割性能。
摘要：
医学图像分割在诊断、治疗规划和医疗保健中至关重要，深度学习为其提供了有前景的进展。值得注意的是，卷积神经网络（CNN）擅长捕捉局部图像特征，而Vision Transformer（ViT）则通过多头自注意力机制熟练地建模长距离依赖关系。尽管具有这些优势，CNN和ViT在高效处理医学图像中的长距离依赖关系时都面临挑战，通常需要大量的计算资源。这一问题，加上专家标注的高成本和有限可用性，对实现精确分割构成了重大障碍。为了解决这些挑战，本研究引入了Semi-Mamba-UNet，它将一个纯粹的基于视觉Mamba的U形编码器-解码器架构与一个传统的基于CNN的UNet集成到一个半监督学习（SSL）框架中。这种创新的SSL方法利用两个网络生成伪标签，并在像素级别同时相互交叉监督，其灵感来自于一致性正则化技术。此外，我们引入了一种自监督像素级对比学习策略，该策略采用一对投影器来进一步增强特征学习能力，特别是在无标签数据上。Semi-Mamba-UNet在两个公开可用的分割数据集上进行了全面评估，并与另外七个以基于CNN或ViT的UNet作为骨干网络的SSL框架进行了比较，突出了所提方法的优越性能。Semi-Mamba-Unet的源代码、所有基线SSL框架、基于CNN和ViT的网络以及两个相应的数据集均已公开。
英文摘要：
Medical image segmentation is essential in diagnostics, treatment planning, and healthcare, with deep learning offering promising advancements. Notably, the convolutional neural network (CNN) excels in capturing local image features, whereas the Vision Transformer (ViT) adeptly models long-range dependencies through multi-head self-attention mechanisms. Despite their strengths, both the CNN and ViT face challenges in efficiently processing long-range dependencies in medical images, often requiring substantial computational resources. This issue, combined with the high cost and limited availability of expert annotations, poses significant obstacles to achieving precise segmentation. To address these challenges, this study introduces Semi-Mamba-UNet, which integrates a purely visual Mamba-based U-shaped encoder-decoder architecture with a conventional CNN-based UNet into a semi-supervised learning (SSL) framework. This innovative SSL approach leverages both networks to generate pseudo-labels and cross-supervise one another at the pixel level simultaneously, drawing inspiration from consistency regularisation techniques. Furthermore, we introduce a self-supervised pixel-level contrastive learning strategy that employs a pair of projectors to enhance the feature learning capabilities further, especially on unlabelled data. Semi-Mamba-UNet was comprehensively evaluated on two publicly available segmentation dataset and compared with seven other SSL frameworks with both CNN- or ViT-based UNet as the backbone network, highlighting the superior performance of the proposed method. The source code of Semi-Mamba-Unet, all baseline SSL frameworks, the CNN- and ViT-based networks, and the two corresponding datasets are made publicly accessible.
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。
