Papers › ViM-UNet: Vision Mamba for Biomedical Segmentation

ViM-UNet: Vision Mamba for Biomedical Segmentation

11 Apr 2024arXiv:2404.07705archive 2025-07-28

Anwai Archit, Constantin Pape

CNNs, most notably the UNet, are the default architecture for biomedical segmentation. Transformer-based approaches, such as UNETR, have been proposed to replace them, benefiting from a global field of view, but suffering from larger runtimes and higher parameter counts. The recent Vision Mamba architecture offers a compelling alternative to transformers, also providing a global field of view, but at higher efficiency. Here, we introduce ViM-UNet, a novel segmentation architecture based on it and compare it to UNet and UNETR for two challenging microscopy instance segmentation tasks. We find that it performs similarly or better than UNet, depending on the task, and outperforms UNETR while being more efficient. Our code is open source and documented at https://github.com/constantinpape/torch-em/blob/main/vimunet.md.

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Instance Segmentation Mamba Segmentation Semantic Segmentation

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Methods

1x1 Convolution Attention Batch Normalization Concatenated Skip Connection Convolution Dense Connections Linear Layer Max Pooling Multi-Head Attention Position-Wise Feed-Forward Layer ReLU Residual Connection Softmax U-Net UNETR

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