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 by an average score of 3.58%. The code and models of Swin-UMamba are publicly available at: https://github.com/JiarunLiu/Swin-UMamba

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，现有方法在长程全局信息建模方面面临挑战：卷积神经网络（CNN）受限于局部感受野，而视觉Transformer（ViT）则受困于注意力机制的高二次复杂度。近期，基于Mamba的模型因其在长序列建模中的卓越能力而受到广泛关注。多项研究表明，这类模型在各类任务中的表现可超越主流视觉模型，同时具备更高精度、更低内存消耗与计算负担。然而，现有Mamba模型大多采用从头训练的方式，尚未探索预训练技术的潜力——而预训练已被证实对数据高效的医学图像分析极为有效。本文提出一种新型Mamba模型Swin-UMamba，该模型专为医学图像分割任务设计，并利用了基于ImageNet预训练的优势。我们的实验结果揭示了ImageNet预训练在提升Mamba模型性能中的关键作用。与CNN、ViT及最新Mamba模型相比，Swin-UMamba展现出显著优越的性能。值得注意的是，在AbdomenMRI、内窥镜与显微图像数据集上，Swin-UMamba较最接近的同类模型U-Mamba平均得分提升3.58%。Swin-UMamba的代码与模型已公开于：https://github.com/JiarunLiu/Swin-UMamba