Skip to content
FMRI data augmentation via synthesis, The IEEE International Symposium on Biomedical Imaging (ISBI'19)
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
misc
tflib
README.md
__init__.py
dev_data_list.pkl
icw_gan.py
log.pkl
ms_ssim.py
test_data_list.pkl
train_data_list.pkl

README.md

ICW-GANs

This is a Tensorflow implementation of my paper:

FMRI data augmentation via synthesis, The IEEE International Symposium on Biomedical Imaging (ISBI'19)

Peiye Zhuang, Alexander Schwing, and Sanmi Koyejo

Results

Prerequisites

  • Tensorflow 1.x
  • Python3
  • NVIDIA GPU + CUDA CuDNN
  • Scipy 1.1.0, Nilearn etc.

Data

We used a public dataset BrainPedia 1952. You may either download the dataset by yourself or use our preprocessed data on GoogleDrive.

Pretrained models

You may download our pretrained model checkpoint on GoogleDrive.

Training & testing

 python icw_gans.py

You may change default parameter settings in the argparse. We did not write an independent python file for testing. Instead, we used the function save_test_img in the code to save test images after amount of training epochs.

Citation

If you use this code for your research, please cite our paper:

@article{icwgans,
  title={FMRI data augmentation via synthesis},
  author={Peiye Zhuang, Alexander Schwing, Sanmi Koyejo},
  journal={The IEEE International Symposium on Biomedical Imaging (ISBI)},
  year={2019}
}
You can’t perform that action at this time.