CausalAD

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[TMI 2025] Collaborative Learning of Augmentation and Disentanglement for Semi-Supervised Domain Generalized Medical Image Segmentation

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최종 버전 다운로드 (.zip)

- - dataset.py
  - transforms.py
- eval.py
- train_causalad.py
- utils.py
- __init__.py
- networks.py
- __init__.py
- logger.py
- loss_functions.py
- mask_generator.py
- model_init.py
- self_paced_learning.py
- torch_utils.py
README.md

// repository documentation

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# CausalAD

This repo is the PyTorch implementation for the paper:

**["Collaborative Learning of Augmentation and Disentanglement for Semi-Supervised Domain Generalized Medical Image Segmentation"](https://ieeexplore.ieee.org/document/11115113)**

## Usage

### 0. Requirements
The code is developed using Python 3.7 with PyTorch 1.11.0.
All experiments in our paper were conducted on a single NVIDIA Quadro RTX 6000 GPU with 24G GPU memory.

Install the main packages:
```angular2html
pytorch == 1.11.0
torchvision == 0.12.0
```

### 1. Data Preparation
#### 1.1. Download data
The dataset can be downloaded from the following links:
* CVC-ClinicDB Dataset - [Link](https://polyp.grand-challenge.org/CVCClinicDB/) 
* Kvasir SEG Dataset - [Link](https://datasets.simula.no/kvasir-seg/)

PS: *Please cite the original dataset papers when using the data in your publications.*

#### 1.2. Split Dataset
Following the list files (within the "*data*" folder) to split the datasets

### 2. Training
```angular2html
python train_causalad.py
```

### 3. Evaluation
```angular2html
python eval.py
```

## Citation
If you find this project useful, please consider citing:
```
@article{shen2025collaborative,
  title={Collaborative Learning of Augmentation and Disentanglement for Semi-Supervised Domain Generalized Medical Image Segmentation},
  author={Shen, Zhiqiang and Cao, Peng and Zhou, Qinghua and Yang, Jinzhu and Zaiane, Osmar R},
  journal={IEEE Transactions on Medical Imaging},
  year={2025},
  publisher={IEEE}
}
```

## Contact
If you have any questions or suggestions, please feel free to contact me ([xxszqyy@gmail.com](xxszqyy@gmail.com)).

## Acknowledgements
* [BayeSeg](https://github.com/obiyoag/BayeSeg)
* [CMIDG](https://github.com/cheng-01037/Causality-Medical-Image-Domain-Generalization)
* [EPL_SemiDG](https://github.com/XMed-Lab/EPL_SemiDG)
* [FACT](https://github.com/MediaBrain-SJTU/FACT)
* [GenericSSL](https://github.com/xmed-lab/GenericSSL)
* [MixStyle](https://github.com/KaiyangZhou/mixstyle-release)

Many thanks to these awesome open-source projects.

CausalAD

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