Accurate medical image segmentation demands the integration of multi-scale information, spanning from local features to global dependencies. However, it is challenging for existing methods to model long-range global information, where convolutional neural networks (CNNs) are constrained by their local receptive fields, and vision transformers (ViTs) suffer from high quadratic complexity of their attention mechanism. Recently, Mamba-based models have gained great attention for their impressive ability in long sequence modeling. Several studies have demonstrated that these models can outperform popular vision models in various tasks, offering higher accuracy, lower memory consumption, and less computational burden. However, existing Mamba-based models are mostly trained from scratch and do not explore the power of pretraining, which has been proven to be quite effective for data-efficient medical image analysis. This paper introduces a novel Mamba-based model, Swin-UMamba, designed specifically for medical image segmentation tasks, leveraging the advantages of ImageNet-based pretraining. Our experimental results reveal the vital role of ImageNet-based training in enhancing the performance of Mamba-based models. Swin-UMamba demonstrates superior performance with a large margin compared to CNNs, ViTs, and latest Mamba-based models. Notably, on AbdomenMRI, Encoscopy, and Microscopy datasets, Swin-UMamba outperforms its closest counterpart U-Mamba_Enc by an average score of 2.72%.

翻译：精确的医学图像分割要求整合从局部特征到全局依赖的多尺度信息。然而，现有方法在处理长程全局信息时面临挑战：卷积神经网络（CNN）受限于局部感受野，而视觉Transformer（ViT）则因其注意力机制的高二次复杂度而受影响。近年来，基于Mamba的模型因其在长序列建模中的出色能力而备受关注。多项研究表明，这类模型能在不同任务中超越主流视觉模型，具有更高的准确率、更低的内存消耗和更少的计算负担。然而，现有的Mamba模型大多从零开始训练，尚未探索预训练（已被证明对数据高效的医学图像分析非常有效）的潜力。本文提出了一种新颖的基于Mamba的模型——Swin-UMamba，该模型专门针对医学图像分割任务设计，并利用了基于ImageNet的预训练优势。实验结果揭示了基于ImageNet的训练对提升Mamba模型性能的关键作用。与CNN、ViT以及最新的Mamba模型相比，Swin-UMamba以显著优势展现出更优性能。值得注意的是，在AbdominalMRI、Encoscopy和Microscopy数据集上，Swin-UMamba较其最接近的同类模型U-Mamba_Enc平均得分高出2.72%。