ASP-VMUNet

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This repository is to store the code of paper: ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation

ASP-VMUNet 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation

### Authors:
- **[Muyi Bao](https://github.com/BaoBao0926/BaoBao0926.github.io)**, **Shuchang Lyu**, **Zhaoyang Xu**, **Qi Zhao**, **Changyu Zeng**, **Wenpei Bai**, **Guangliang Cheng**

### NEWS:
- 2025.04.05: The code was uploaded
- 2025.03.25: Paper was uploaded in [arXiv](https://arxiv.org/abs/2503.19427)
- 2024.12.16: The repository was created.

### Environments

## 0. Main Environments
The environment configuration refers [VM-UNet](https://github.com/JCruan519/VM-UNet).
```bash
conda create -n vmunet python=3.8
conda activate vmunet
pip install torch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117
pip install packaging
pip install timm==0.4.12
pip install pytest chardet yacs termcolor
pip install submitit tensorboardX
pip install triton==2.0.0
pip install causal_conv1d==1.0.0  # causal_conv1d-1.0.0+cu118torch1.13cxx11abiFALSE-cp38-cp38-linux_x86_64.whl
pip install mamba_ssm==1.0.1  # mmamba_ssm-1.0.1+cu118torch1.13cxx11abiFALSE-cp38-cp38-linux_x86_64.whl
pip install scikit-learn matplotlib thop h5py SimpleITK scikit-image medpy yacs
```
The .whl files of causal_conv1d and mamba_ssm could be found [here](https://pan.baidu.com/s/1Tibn8Xh4FMwj0ths8Ufazw?pwd=uu5k)

# Abstract
Skin lesion segmentation is a critical challenge in computer vision, essential to accurately separate pathological features from healthy skin for diagnostics. Traditional Convolutional Neural Networks (CNNs) are limited by narrow receptive fields, and Transformers face significant computational burdens. This paper presents a novel skin lesion segmentation framework, the Atrous Shifted Parallel Vision Mamba UNet (ASP-VMUNet), which integrates the efficient and scalable Mamba architecture to overcome limitations in traditional CNNs and computationally demanding Transformers. The framework introduces an atrous scan technique that minimizes background interference and expands the receptive field, enhancing Mamba's scanning capabilities. Additionally, the inclusion of a Parallel Vision Mamba (PVM) layer and a shift round operation optimizes feature segmentation and fosters rich inter-segment information exchange. A supplementary CNN branch with an Selective-Kernel (SK) Block further refines the segmentation by blending local and global contextual information. Tested on four benchmark datasets (ISIC16/17/18 and PH2), ASP-VMUNet demonstrates superior performance in skin lesion segmentation, validated by comprehensive ablation studies. This approach not only advances medical image segmentation but also highlights the benefits of hybrid architectures in medical imaging technology.

# Experimental Results
### Comparison on Benchmark
<img src="https://github.com/BaoBao0926/ASP-VMUNet/blob/main/figure/ComparsionResult.png" alt="ASP_Layer" width="600"/>

### Ablation Study of Component
<img src="https://github.com/BaoBao0926/ASP-VMUNet/blob/main/figure/AS_component.png" alt="AS_component" width="600"/>

### Ablation Study of Component
<img src="https://github.com/BaoBao0926/ASP-VMUNet/blob/main/figure/AS_atrousstep.png" alt="AS_atrousstep" width="600"/>

### Ablation Study of Atrous Scan and The Number of Encoder Layers
<img src="https://github.com/BaoBao0926/ASP-VMUNet/blob/main/figure/AS_atrousscan_layer.png" alt="AS_atrousscan_layer" width="600"/>

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