VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation
Mingya Zhang Yue Yu Limei Gu Tingsheng Lin Xianping Tao
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.