augmentation

augmentation copied to clipboard

= Adversarial Augmentation for Enhancing Classification of Mammography Images

=== https://arxiv.org/abs/1902.07762[Paper]

https://github.com/BreastGAN/augmentation[Adversarial Augmentation for Enhancing Classification of Mammography Images]

https://www.researchgate.net/profile/Lukas_Jendele[Lukas Jendele]* 1), https://oskopek.com/[Ondrej Skopek]* 1), https://www.researchgate.net/profile/Anton_Becker2[Anton S. Becker] 2,3), http://www.vision.ee.ethz.ch/~kender/[Ender Konukoglu] 4)

1. Department of Computer Science, ETH Zurich
2. Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich; Zurich, Switzerland
3. Department of Health Sciences and Technology, ETH Zurich; Zurich, Switzerland
4. Computer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich

In arXiv, 2019. (* joint contribution)

Correspondence to: mailto:[email protected][Lukas Jendele] and mailto:[email protected][Ondrej Skopek]

== Citation If you use this code for your research, please cite our https://arxiv.org/abs/1902.07762[paper]:

@article{AdvAugmentation2019,
  title={{Adversarial Augmentation for Enhancing Classification of Mammography Images}},
  author={Jendele, Lukas and Skopek, Ondrej and Becker, Anton S and Konukoglu, Ender},
  journal={arXiv preprint arXiv:1902.07762},
  year={2019}
}

== Related Projects

https://github.com/junyanz/CycleGAN[CycleGAN]: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.

https://github.com/BreastGAN[BreastGAN]: Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks

== Requirements and versions:

• Python 3.5
• Git
• Tensorflow 1.12.0

Important: When committing, remember to be in the virtual environment, for hooks to work.

NOTE: All code in Jupyter Notebooks is purely experimental. Use at your own risk.

== Setup

Make sure there is no venv/ directory in your repository. If there is, remove it. Run the following commands:

./setup/create_venv.sh
source venv/bin/activate

Important: For all commands here, we assume you are sourced into the virtual environment: source venv/bin/activate

== Running the experiments

=== Image conversion

Put all data into the directories in data_in/. Supported are: 1_BCDR/, 2_INbreast/, 3_zrh/, cbis.

1. ./local/convert_images_all.sh
2. ./local/merge_images_all.sh
3. ./local/split_images_all.sh
4. ./local/treval_split.sh

=== GAN training

1. ./local/run.sh. Wait 24 hours.
2. ./local/infer.sh. Make sure to enter the correct checkpoint number here and below.
3. ./local/to_png.sh. Make sure to change the paths in notebooks/inference_tfrecord_to_png.py.

== Jupyter notebooks

NOTE: All code in Jupyter Notebooks is purely experimental. Use at your own risk.

Save notebooks in the notebooks/ directory. These can also be worked on locally using Jupyter. In the project root directory, you can run either:

• jupyter notebook,
• or jupyter lab.

Add the following cell to your notebook, ideally in a "section":

# noqa
import os
wd = %pwd
print('Current directory:', wd)
if wd.endswith('notebooks'):
    %cd ..

== Directory structure

• cluster/ -- scripts for running the training/evaluation on the cluster
• data_in/ -- input data and associated scripts/configs
• data_out/ -- output data and logs + associated scripts/configs
• local/ -- scripts for running the training/evaluation locally
• models/ -- scripts defining the models + hyperparameters
• notebooks/ -- data exploration and other rapid development notebooks ** Models from here should eventually be promoted into models/
• resources/ -- Python utilities
• setup/ -- environment setup and verification scripts in Python/Bash
• venv/ -- the (local) Python virtual environment

== Formatting

Run: ./setup/clean.sh. A Git hook will tell you if any files are misformatted before committing.

== Third Party code used in this project

=== ICNR

https://github.com/kostyaev/ICNR by Dmitry Kostyaev

Licensed under the MIT Licence.

In: models/utils/icnr.py

=== Tensor2Tensor

https://github.com/tensorflow/tensor2tensor by The Tensor2Tensor Authors.

Licensed under the Apache License Version 2.0.

In: models/breast_cycle_gan

=== TensorFlow, TensorFlow Models

https://github.com/tensorflow/tensorflow, https://github.com/tensorflow/models by The TensorFlow Authors.

Licensed under the Apache License Version 2.0.

In: models/breast_cycle_gan

=== TensorPack

https://github.com/tensorpack/tensorpack by Yuxin Wu.

Licensed under the Apache License Version 2.0.

In: models/rcnn