VM-UNet: Vision Mamba UNet for Medical Image Segmentation
Jiacheng Ruan Suncheng Xiang
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.