Skip to content
/ CausalAD Public

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

Notifications You must be signed in to change notification settings

Senyh/CausalAD

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 
 
 

Repository files navigation

CausalAD

This repo is the PyTorch implementation for the paper:

"Collaborative Learning of Augmentation and Disentanglement for Semi-Supervised Domain Generalized Medical Image Segmentation"

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:

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
  • Kvasir SEG Dataset - Link

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

python train_causalad.py

3. Evaluation

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 ([email protected]).

Acknowledgements

Many thanks to these awesome open-source projects.

About

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

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages