ViM-UNet: 用于生物医学分割的Vision Mamba
ViM-UNet: Vision Mamba for Biomedical Segmentation
AI总结：
针对生物医学分割中UNet视野有限、Transformer类方法效率低的问题，提出基于Vision Mamba的ViM-UNet分割架构，在两项显微镜实例分割任务上性能优于UNETR且效率更高，表现与UNet相当或更优。
摘要：
以UNet为代表的CNN是生物医学分割的默认架构。基于Transformer的方法（如UNETR）因具备全局视野被提出以替代CNN，但存在运行时间更长、参数量更高的问题。近期的Vision Mamba架构为Transformer提供了极具吸引力的替代方案，它同样具备全局视野，但效率更高。本文提出基于该架构的新型分割架构ViM-UNet，并在两项具有挑战性的显微镜实例分割任务上将其与UNet和UNETR对比。结果显示，ViM-UNet的表现依任务不同与UNet相当或更优，且性能优于UNETR同时效率更高。本文代码已开源，文档见https://github.com/constantinpape/torch-em/blob/main/vimunet.md。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
