LoG-VMamba

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LoG-VMamba: Local-Global Vision Mamba for Medical Image Segmentation

// repository documentation

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# <a href=https://arxiv.org/pdf/2408.14415>LoG-VMamba</a> <img src="imgs/icon1.jpeg" alt="Violet Mamba on a Log" width="70"/>
LoG-VMamba: Local-Global Vision Mamba for Medical Image Segmentation

Arxiv: <a href=https://arxiv.org/abs/2408.14415>Link</a>

![LoG-VMamba](https://raw.githubusercontent.com/imedslab/LoG-VMamba/main/imgs/LoG_VMamba.png "LoG-VMamba")

![LTX GTX](https://raw.githubusercontent.com/imedslab/LoG-VMamba/main/imgs/LTX_GTX.png "Local-Global Token extractors")

![Comparisons](https://raw.githubusercontent.com/imedslab/LoG-VMamba/main/imgs/LoG_VMamba_results.png "Comparisons")

## Dependencies

We provide file `env.yaml` for dependencies.

## Installation

```bash
conda env create -f env.yaml
conda activate log_vmamba
pip install -e .
```

## Split data
```bash
cd mlpipeline/utils
python split_brats.py
```

## Training
```bash
python -m mlpipeline.train.run experiment=${EXP_NAME} \
        model.params.cfg.arch=${ARCH_NAME}
```
where
- <EXP_NAME>: experiment setting can be `brats_sem_seg`.
- <ARCH_NAME>: architecture name can be `UMambaEncDC_K1`.

LoG-VMamba

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