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# Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss

This repository contains the code for CNN/WGAN-MSE/VGG network introduced in the following paper

[Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss](https://ieeexplore.ieee.org/abstract/document/8340157/)

## Installation
Make sure you have [Python](https://www.python.org/) installed, then install [TensorFlow](https://www.tensorflow.org/install/) on your system.

## Usage

### Prepare the training data

In order to start the training process, please prepare your ``training data`` in the following form:

* ``data``: N x W x H
* ``label``: N x W x H

Here N, W, and H are number, depth, width, and height of the input data, respectively. Then ``data`` and ``label`` are stored in a ``hdf5`` file.

### Pre-trained VGG model

Please also download the pre-trained VGG model from [here](https://github.com/machrisaa/tensorflow-vgg).

### Training network
```
python train_cnn.py
python train_wgan.py
```

## Contact

Email: qs dot yang18 at gmail dot com

Any discussions, suggestions and questions are welcome!

## Citation

```
@article{ldct_wgan_perceptual_loss,
  author={Q. Yang and P. Yan and Y. Zhang and H. Yu and Y. Shi and X. Mou and M. K. Kalra and Y. Zhang and L. Sun and G. Wang},
  journal={IEEE Transactions on Medical Imaging},
  title={Low Dose {CT} Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss},
  year={2018},
  volume={37},
  number={6},
  pages={1348-1357},
  doi={10.1109/TMI.2018.2827462},
  ISSN={0278-0062},
  month={june},
}
```

## Requirements
Python
Tensorflow

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