- numpy==1.18.5
- scikit_image==0.16.2
- torchvision==0.8.1
- torch==1.7.0
- runstats==1.8.0
- pytorch_lightning==1.0.6
- h5py==2.10.0
- PyYAML==5.4
Train
cd experimental/SANet/
sbatch job.sh
Change other arguments that you can train your own model.
Parameter settings
The detailed parameter settings can be find in our arXiv paper: https://arxiv.org/pdf/2109.01664.pdf
Citation
If you find SANet useful for your research, please consider citing the following papers:
@inproceedings{feng2021MINet,
title={Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network},
author={Feng, Chun-Mei and Fu, Huazhu and Yuan, Shuhao and Xu, Yong},
booktitle={International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)},
year={2021}
}
@article{feng2021exploring,
title={Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution},
author={Feng, Chun-Mei and Yan, Yunlu and Liu, Chengliang and Fu, Huazhu and Xu, Yong and Shao, Ling},
journal={arXiv preprint arXiv:2109.01664},
year={2021}
}