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.
Code (1)
Tasks
Image SegmentationMambaMedical Image SegmentationSemantic SegmentationState Space ModelsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation
UNet and its variants have been widely used in medical image segmentation. However, these models, especially those based on Transformer architectures, pose challenges due to their large number of parameters and computati…
Image SegmentationMambaMedical Image SegmentationSemantic Segmentation+1ViM-UNet: Vision Mamba for Biomedical Segmentation
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 sufferin…
Instance SegmentationMambaSegmentationSemantic SegmentationWeak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation
Medical image segmentation is increasingly reliant on deep learning techniques, yet the promising performance often come with high annotation costs. This paper introduces Weak-Mamba-UNet, an innovative weakly-supervised …
Cardiac SegmentationDecoderImage SegmentationMamba+4LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation
In clinical practice, medical image segmentation provides useful information on the contours and dimensions of target organs or tissues, facilitating improved diagnosis, analysis, and treatment. In the past few years, co…
Image SegmentationLong-range modelingMambaMedical Image Segmentation+1Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation
In recent advancements in medical image analysis, Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have set significant benchmarks. While the former excels in capturing local features through its convolu…
Cardiac SegmentationComputational EfficiencyDecoderImage Segmentation+5