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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 with fewer convolution layers to save calculation cost. 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, e.g. obtaining 89.03, 89.71 and 81.08 in terms of DSC score on three datasets respectively. 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
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Academic Publication VM-UNet: Vision Mamba UNet for Medical Image Segmentation
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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, wherea...
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Frequently Asked Questions (FAQ)
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What is the core focus of the research titled 'VM-UNet: Vision Mamba UNet for Medical Image Segmentation'?
This literature focuses on: 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 com...
Are there open-source GitHub repositories related to VM-UNet: Vision Mamba UNet for Medical Image Segmentation?
Yes, open-source projects like fikrikarim/parlor (On-device, real-time multimodal AI. Have natural voice and vision conversations with an AI that runs entirely on your machine. Powered by Gemma 4 E...) are actively building upon these concepts.
Which startups are commercializing the technology behind VM-UNet: Vision Mamba UNet for Medical Image Segmentation?
Products like Zzzappy are bringing this to market. Their focus is: Science-backed breaks to protect your vision & prevent RSI.
What other academic literature is closely related to 'VM-UNet: Vision Mamba UNet for Medical Image Segmentation'?
Yes, highly correlated activity was mapped. An entry titled 'VM-UNet: Vision Mamba UNet for Medical Image Segmentation' discusses this: In the realm of medical image segmentation, both CNN-based and Transformer-based models have been extensively explored. However, CNNs exhibit limit...
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GitHubfikrikarim/parlor
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GitHubjmerelnyc/Photo-agents
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Product HuntZzzappy
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