# VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2403.09157 · HTML: https://mlsanity.com/paper/2403.09157/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 20 of its 27 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for medical image analysis](https://mlsanity.com/ssm/mamba-for-medical-image-analysis/)

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation](https://mlsanity.com/paper/2211.01784/) (2022)
- [TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation](https://mlsanity.com/paper/2208.00713/) (2022)
- [TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model](https://mlsanity.com/paper/2207.13415/) (2022)
- [U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2311.17791/) (2023)

## Built on by (most cited first, 30 of 39)

- [Fusion-Mamba for Cross-modality Object Detection](https://mlsanity.com/paper/2404.09146/) (2024)
- [State Space Model for New-Generation Network Alternative to Transformers: A Survey](https://mlsanity.com/paper/2404.09516/) (2024)
- [Vision Mamba: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2405.04404/) (2024)
- [A Survey on Visual Mamba](https://mlsanity.com/paper/2404.15956/) (2024)
- [UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation](https://mlsanity.com/paper/2403.20035/) (2024)
- [A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond](https://mlsanity.com/paper/2410.02362/) (2024)
- [AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation](https://mlsanity.com/paper/2501.07810/) (2025)
- [ProMamba: Prompt-Mamba for polyp segmentation](https://mlsanity.com/paper/2403.13660/) (2024)
- [Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical Image Segmentation](https://mlsanity.com/paper/2403.17701/) (2024)
- [xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart](https://mlsanity.com/paper/2407.01530/) (2024)
- [Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2504.17515/) (2025)
- [HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation](https://mlsanity.com/paper/2408.11289/) (2024)
- [Topology-aware Mamba for Crack Segmentation in Structures](https://mlsanity.com/paper/2410.19894/) (2024)
- [MUCM-Net: A Mamba Powered UCM-Net for Skin Lesion Segmentation](https://mlsanity.com/paper/2405.15925/) (2024)
- [SkinMamba: A Precision Skin Lesion Segmentation Architecture with Cross-Scale Global State Modeling and Frequency Boundary Guidance](https://mlsanity.com/paper/2409.10890/) (2024)
- [A Comprehensive Analysis of Mamba for 3D Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2503.19308/) (2025)
- [MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation](https://mlsanity.com/paper/2604.20286/) (2026)
- [A Reverse Mamba Attention Network for Pathological Liver Segmentation](https://mlsanity.com/paper/2502.18232/) (2025)
- [MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation](https://mlsanity.com/paper/2410.23738/) (2024)
- [Venturing into Uncharted Waters: The Navigation Compass from Transformer to Mamba](https://mlsanity.com/paper/2406.16722/) (2024)
- [BS-Mamba for Black-Soil Area Detection On the Qinghai-Tibetan Plateau](https://mlsanity.com/paper/2503.12495/) (2025)
- [ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation](https://mlsanity.com/paper/2503.19427/) (2025)
- [UNet with Self-Adaptive Mamba-Like Attention and Causal-Resonance Learning for Medical Image Segmentation](https://mlsanity.com/paper/2505.15234/) (2025)
- [CMFDNet: Cross-Mamba and Feature Discovery Network for Polyp Segmentation](https://mlsanity.com/paper/2508.17729/) (2025)
- [LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation](https://mlsanity.com/paper/2405.15779/) (2024)
- [When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion Segmentation](https://mlsanity.com/paper/2411.09363/) (2024)
- [SSNet: Saliency Prior and State Space Model-based Network for Salient Object Detection in RGB-D Images](https://mlsanity.com/paper/2503.02270/) (2025)
- [Prompt-Guided Dual-Path UNet with Mamba for Medical Image Segmentation](https://mlsanity.com/paper/2503.19589/) (2025)
- [MambaCAFU: Hybrid Multi-Scale and Multi-Attention Model with Mamba-Based Fusion for Medical Image Segmentation](https://mlsanity.com/paper/2510.03786/) (2025)
- [WDFFU-Mamba: A Wavelet-guided Dual-attention Feature Fusion Mamba for Breast Tumor Segmentation in Ultrasound Images](https://mlsanity.com/paper/2512.17278/) (2025)

## Abstract (arXiv)

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.