VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation
<|reference_start|>VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation: In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated. However, CNNs have limited modeling capabilities for long-range dependencies, making it challenging to exploit the semantic information within images fully. On the other hand, the quadratic computational complexity poses a challenge for Transformers. Recently, State Space Models (SSMs), such as Mamba, have been recognized as a promising method. They not only demonstrate superior performance in modeling long-range interactions, but also preserve a linear computational complexity. Inspired by the Mamba architecture, We proposed Vison Mamba-UNetV2, the Visual State Space (VSS) Block is introduced to capture extensive contextual information, the Semantics and Detail Infusion (SDI) is introduced to augment the infusion of low-level and high-level features. We conduct comprehensive experiments on the ISIC17, ISIC18, CVC-300, CVC-ClinicDB, Kvasir, CVC-ColonDB and ETIS-LaribPolypDB public datasets. The results indicate that VM-UNetV2 exhibits competitive performance in medical image segmentation tasks. Our code is available at https://github.com/nobodyplayer1/VM-UNetV2.<|reference_end|>
@article{zhang2024vm-unet-v2,
    title={VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation},
    author={Mingya Zhang, Yue Yu, Limei Gu, Tingsheng Lin, Xianping Tao},
    journal={arXiv preprint arXiv:2403.09157},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09157},
    primaryClass={eess.IV cs.CV}
}