bibtext: @InProceedings{ Liu_SwinUMamba_MICCAI2024,
                   author = { Liu, Jiarun and Yang, Hao and Zhou, Hong-Yu and Xi, Yan and Yu, Lequan and Li, Cheng and Liang, Yong and Shi, Guangming and Yu, Yizhou and Zhang, Shaoting and Zheng, Hairong and Wang, Shanshan },
                   title = { { Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 are constrained by their local receptive fields, and vision transformers 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 2.72%. The code and models of Swin-UMamba are publicly available at: https://github.com/JiarunLiu/Swin-UMamba.
title: Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining
authors: Liu, Jiarun Yang, Hao Zhou, Hong-Yu Xi, Yan Yu, Lequan Li, Cheng Liang, Yong Shi, Guangming Yu, Yizhou Zhang, Shaoting Zheng, Hairong Wang, Shanshan