MSVM-UNet：用于医学图像分割的多尺度Vision Mamba UNet
MSVM-UNet: Multi-Scale Vision Mamba UNet for Medical Image Segmentation
AI总结：
提出MSVM-UNet模型，通过在VSS块引入多尺度卷积和LKPE层，有效捕获多尺度特征并解决2D图像方向敏感性问题，在医学图像分割任务上优于现有方法。
摘要：
状态空间模型（SSMs），尤其是Mamba，因其能以线性计算复杂度建模长距离依赖关系，在医学图像分割中展现出巨大潜力。然而，准确的医学图像分割需要有效学习多尺度细节特征表示和全局上下文依赖关系。尽管现有工作试图通过结合CNNs和SSMs以利用各自优势来解决此问题，但它们既未设计专门的模块来有效捕获多尺度特征表示，也未充分解决将Mamba应用于2D图像数据时的方向敏感性问题。为克服这些局限，我们提出了一种用于医学图像分割的多尺度Vision Mamba UNet模型，称为MSVM-UNet。具体而言，通过在VSS块中引入多尺度卷积，我们能更有效地从VMamba编码器的分层特征中捕获并聚合多尺度特征表示，并更好地处理2D视觉数据。此外，大核块扩展（LKPE）层通过同时整合空间和通道信息，实现了更高效的特征图上采样。在Synapse和ACDC数据集上的大量实验表明，我们的方法在捕获和聚合多尺度特征表示以及建模像素间长距离依赖关系方面，比一些最先进的方法更有效。
英文摘要：
State Space Models (SSMs), especially Mamba, have shown great promise in medical image segmentation due to their ability to model long-range dependencies with linear computational complexity. However, accurate medical image segmentation requires the effective learning of both multi-scale detailed feature representations and global contextual dependencies. Although existing works have attempted to address this issue by integrating CNNs and SSMs to leverage their respective strengths, they have not designed specialized modules to effectively capture multi-scale feature representations, nor have they adequately addressed the directional sensitivity problem when applying Mamba to 2D image data. To overcome these limitations, we propose a Multi-Scale Vision Mamba UNet model for medical image segmentation, termed MSVM-UNet. Specifically, by introducing multi-scale convolutions in the VSS blocks, we can more effectively capture and aggregate multi-scale feature representations from the hierarchical features of the VMamba encoder and better handle 2D visual data. Additionally, the large kernel patch expanding (LKPE) layers achieve more efficient upsampling of feature maps by simultaneously integrating spatial and channel information. Extensive experiments on the Synapse and ACDC datasets demonstrate that our approach is more effective than some state-of-the-art methods in capturing and aggregating multi-scale feature representations and modeling long-range dependencies between pixels.
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。
