gans v1.0

Deep Convolutional Generative Adversarial Network (DCGAN*) implemented in PyTorch.

Applied on different medical datasets for different medical-based approaches (later we will be discussing Segmentation).

Unlike the original paper "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks" we generate 128 x 128 Images that looks quite plausible.Check the results above and try it yourself.

Motivation

The main objective of this work is twofold :

• Generate synthetic medical images that model the distribution of the input data
• [SOON] Use both of the synthetic and real images for training a context-aware CNN that can accurately segment the given images

Install

In your terminal, run:

git clone https://github.com/AissamDjahnine/gans
cd gans

Then, install all dependencies with PIP using:

pip install -r requirements.txt

Datasets

Medical datasets :

Since the main task behind this project is to generate synthetic medical images , we used these two datasets described below ( links are available as well )

* Absorbance microscopy images of human Retinal Pigment Epithelium (RPE) cells

You can use this Dataset consisted of 1032 ( 256 x 256 ) RPE cells images images.You can browse and donwload the data using this link : RpeCells

* Osteosarcoma data from UT Southwestern/UT Dallas for Viable and Necrotic Tumor Assessment

The dataset consists of 1144 images of size 1024 X 1024 at 10X resolution with the following distribution: 536 (47%) non-tumor images, 263 (23%) necrotic tumor images and 345 (30%) viable tumor tiles.

You can browse the data using this link : Osteosarcoma data from UT Southwestern/UT Dallas, to download the data that you can use directly: Osteosarcoma data

Usage

useful notes :

• You can train the model on big datasets.The script saves your current model state (weights , bias ..) so that you can resume training from this point.the state is saved after 25 epochs (you can adjust the value depending on how big is your dataset) --Available only for the API.

• Please respect this structure while feeding the model with your images directory :

./IMAGESFOLDER/Images/.. Examlpe : RPEDATA/Images/.. , Osteosarcoma_data/Images/..

Jupyter Notebook

Run a jupyter process :

Jupyter notebook

You find a Jupyter notebook version of the project , you can interactively run cells and check outputs for different sections

In your terminal, run:

mkdir Generated_Images_numpy_notebook

To create a directory for the generated imges ( numpy arrays ) version.

API

In your terminal, run:

python GAN_F_128.py --path IMAGEFOLDER --epochs 500 --batch-size 128 --lr 0.0002

Note : You may use default values ( see the file : GAN_F_128.py )

Example :

python GAN_F_128.py --path ./RPEDATA --epochs 500 

Visualization

Jupyter notebook

• You can see generated images changing with time ( every 10 batches ), feel free to change the display time.

Tensorboard

If you're familiar with Tensorboard , skip this section

In your terminal, run:

tensorboard --logdir ./runs

• You should be looking to :

References

• "Generative Adversarial Networks." Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. ArXiv 2014.

• "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks" Alec Radford, Luke Metz, Soumith Chintala

Questions :

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please feel free to contact if case of issues.