CycleMix

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[CVPR 2022] CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision

CycleMix 최신버젼 다운로드

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// repository documentation

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# CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision
This project is developed for our CVPR 2022 paper: [CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision](https://arxiv.org/abs/2203.01475). Our code is implemented based on the [PuzzleMix](https://github.com/snu-mllab/PuzzleMix), but is also applicable to other mixup-based augmentation methods. For more information about CycleMix, please read the following paper:

```
@article{zhang2022cyclemix,
  title={CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision},
  author={Zhang, Ke and Zhuang, Xiahai},
  journal={arXiv preprint arXiv:2203.01475},
  year={2022}
}
```
Please also cite this paper if you are using CycleMix for your research.

# Datasets
1. The MSCMR dataset with mask annotations can be downloaded from [MSCMRseg](https://zmiclab.github.io/zxh/0/mscmrseg19/data.html).
2. Our scribble annotations of MSCMRseg have been released in [MSCMR_scribbles](https://github.com/BWGZK/CycleMix/tree/main/MSCMR_scribbles). Please cite this paper if you use the scribbles for your research.
3. The scribble-annotated MSCMR dataset used for training could be directly downloaded from [MSCMR_dataset](https://github.com/BWGZK/CycleMix/tree/main/MSCMR_dataset). 
4. The ACDC dataset with mask annotations can be downloaded from [ACDC](https://www.creatis.insa-lyon.fr/Challenge/acdc/) and the scribble annotations could be downloaded from [ACDC scribbles](https://vios-s.github.io/multiscale-adversarial-attention-gates/data). Please organize the dataset as the following structure:
```
XXX_dataset/
  -- TestSet/
      --images/
      --labels/
  -- train/
      --images/
      --labels/
  -- val/
      --images/
      --labels/
```

# Usage
1. Set the "dataset" parameter in main.py, line 76, to the name of dataset, i.e., "MSCMR_dataset".
2. Set the "output_dir" in main.py, line 79, as the path to save the checkpoints. 
3. Download the dataset, for example, the [MSCMR_dataset](https://github.com/BWGZK/CycleMix/tree/main/MSCMR_dataset). Then, Set the dataset path in /data/mscmr.py, line 110, to your data path where the dataset is located in.
4. Check your GPU devices and modify the "GPU_ids" parameter in main.py, line 83 and "CUDA_VISIBLE_DEVICES" in run.sh.
5. Start to train by sh run.sh.
```
CUDA_LAUNCH_BLOCKING=1 CUDA_VISIBLE_DEVICES=5 nohup python main.py --mixup_alpha 0.5 --graph True --n_labels 3 --eta 0.2 --beta 1.2 --gamma 0.5 --neigh_size 4 --transport True --t_size 4 --t_eps 0.8 &
```

# Requirements

This code has been tested with  
Python 3.8.5  
torch  1.7.0  
torchvision 0.8.0  
gco-wrapper (https://github.com/Borda/pyGCO)

If you have any problems, please feel free to contact us. Thanks for your attention.

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