Tensorflow implementation of our unsupervised cross-modality domain adaptation framework.
This is the version of our TMI paper.
Please refer to the branch SIFA-v1 for the version of our AAAI paper.
Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation
IEEE Transactions on Medical Imaging
git clone https://github.com/cchen-cc/SIFA
cd SIFA
tfrecord
format to be decoded by ./data_loader.py
. The pre-processed data has been released from our work PnP-AdaNet. The training data can be downloaded here. The testing CT data can be downloaded here. The testing MR data can be downloaded here.tfrecord
data of two domains into corresponding folders under ./data
accordingly../create_datalist.py
to generate the datalists containing the path of each data../config_param.json
./main.py
to start the training process./evaluate.py
./evaluate.py
to start the evaluation.If you find the code useful for your research, please cite our paper.
@article{chen2020unsupervised,
title = {Unsupervised Bidirectional Cross-Modality Adaptation via
Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation},
author = {Chen, Cheng and Dou, Qi and Chen, Hao and Qin, Jing and Heng, Pheng Ann},
journal = {arXiv preprint arXiv:2002.02255},
year = {2020}
}
@inproceedings{chen2019synergistic,
author = {Chen, Cheng and Dou, Qi and Chen, Hao and Qin, Jing and Heng, Pheng-Ann},
title = {Synergistic Image and Feature Adaptation:
Towards Cross-Modality Domain Adaptation for Medical Image Segmentation},
booktitle = {Proceedings of The Thirty-Third Conference on Artificial Intelligence (AAAI)},
pages = {865--872},
year = {2019},
}
Part of the code is revised from the Tensorflow implementation of CycleGAN.