Unsupervised Tumor Characterization via Conditional Generative Adversarial Networks

Package Requirement

pytorch 1.4.0
imgaug 0.4.0
scikit-image 0.14.2
matplotlib 3.0.2
numpy 1.15.4
opencv-python 4.1.2.30

Usage Guideline

• dataset.py defines how the program will receive the data. Use the ColonDataset class defined within as template and modify the internal logic accordingly to adapt to your data.
• config.py contains the general running configuration (#thread, saving locations), for the network running options, please refer to model/opt.py
• trainer.py and inferer.py are the running scripts accordingly.
• stats/get_patch_stat.py contains the code for calculation of all statistics reported in the paper.
• plots.py is script to plot/parse the .npy output by inferer.py to figure.

Citation

If any part of this code is used, please give appropriate citation to our paper.

BibTex entry:

@ARTICLE{9090975,
  author={Q. D. {Vu} and K. {Kim} and J. T. {Kwak}},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={Unsupervised Tumor Characterization via Conditional Generative Adversarial Networks}, 
  year={2020},
  volume={},
  number={},
  pages={1-1},}

Authors

• Quoc Dang Vu

Acknowledgement

Thanks https://github.com/eriklindernoren/PyTorch-GAN for the collections of GAN implementations in pytorch which we are inpsired by.

License

This project is licensed under the MIT License - see the LICENSE file for details