SANet

Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution

Dependencies

  • 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.

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}
}
@inproceedings{feng2021SANet,
  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 e-prints},
  pages={arXiv--2106},
  year={2021}
}

GitHub

https://github.com/chunmeifeng/SANet