VM-UNet: Vision Mamba UNet for Medical Image Segmentation
<|reference_start|>VM-UNet: Vision Mamba UNet for Medical Image Segmentation: In the realm of medical image segmentation, both CNN-based and Transformer-based models have been extensively explored. However, CNNs exhibit limitations in long-range modeling capabilities, whereas Transformers are hampered by their quadratic computational complexity. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as a promising approach. They not only excel in modeling long-range interactions but also maintain a linear computational complexity. In this paper, leveraging state space models, we propose a U-shape architecture model for medical image segmentation, named Vision Mamba UNet (VM-UNet). Specifically, the Visual State Space (VSS) block is introduced as the foundation block to capture extensive contextual information, and an asymmetrical encoder-decoder structure is constructed. We conduct comprehensive experiments on the ISIC17, ISIC18, and Synapse datasets, and the results indicate that VM-UNet performs competitively in medical image segmentation tasks. To our best knowledge, this is the first medical image segmentation model constructed based on the pure SSM-based model. We aim to establish a baseline and provide valuable insights for the future development of more efficient and effective SSM-based segmentation systems. Our code is available at https://github.com/JCruan519/VM-UNet.<|reference_end|>
@article{ruan2024vm-unet:,
    title={VM-UNet: Vision Mamba UNet for Medical Image Segmentation},
    author={Jiacheng Ruan, Jincheng Li, and Suncheng Xiang},
    journal={arXiv preprint arXiv:2402.02491},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02491},
    primaryClass={eess.IV cs.CV}
}