Lukas Jendele* 1), Ondrej Skopek* 1), Anton S. Becker 2,3), Ender Konukoglu 4)
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Department of Computer Science, ETH Zurich
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Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich; Zurich, Switzerland
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Department of Health Sciences and Technology, ETH Zurich; Zurich, Switzerland
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Computer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich
In arXiv, 2019. (* joint contribution)
Correspondence to: Lukas Jendele and Ondrej Skopek
If you use this code for your research, please cite our 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}
}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.
BreastGAN: Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks
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Python 3.5
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Git
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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.
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/activateImportant: For all commands here, we assume you are sourced into
the virtual environment: source venv/bin/activate
Put all data into the directories in data_in/. Supported are: 1_BCDR/, 2_INbreast/, 3_zrh/, cbis.
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./local/convert_images_all.sh -
./local/merge_images_all.sh -
./local/split_images_all.sh -
./local/treval_split.sh
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:
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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 ..-
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/
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resources/— Python utilities -
setup/— environment setup and verification scripts in Python/Bash -
venv/— the (local) Python virtual environment
Run: ./setup/clean.sh.
A Git hook will tell you if any files are misformatted before committing.
https://github.com/kostyaev/ICNR by Dmitry Kostyaev
Licensed under the MIT Licence.
In: models/utils/icnr.py
https://github.com/tensorflow/tensor2tensor by The Tensor2Tensor Authors.
Licensed under the Apache License Version 2.0.
In: models/breast_cycle_gan
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
https://github.com/tensorpack/tensorpack by Yuxin Wu.
Licensed under the Apache License Version 2.0.
In: models/rcnn
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