Left: Real Image, Right: Generated Image
MORE IMAGES AND RESULTS FOUND IN OUR PAPER LINKED HERE

• Generate images of lungs with COVID-19 to model progression of COVID-19
• Use this to expand current datasets, which are limited in quantity
• Eventually create an image-to-image model to see the progression of the disease in one set of lungs

• Model Definition and Training Script
• Demo of our final model, and demo of interpolation to model the progression
• Latent Space Widget for visualization
• Academic paper where we discuss our GAN
• Generated images from the GAN
• Project Resources containing the dataset and our final models can be found here.
  • Models (.h5 files ready to be loaded and used)
  • Data (from https://github.com/UCSD-AI4H/COVID-CT)

• Model definition and training is located in COVID_GAN.ipynb or COVID_GAN.py.
• To run, clone this repo, and download the data set available here.
• Make sure the above-mentioned script and the zip file of data are in the same directory.
• Before running, make sure all dependencies are installed by running pip3 install -r requirements.txt in terminal.
• Ensure the hyperparameter load_data_from_Google_Drive is set to False.
• The program will define a new model and train it with the data provided and hyperparameters specified and changeable in COVID_GAN.ipynb or COVID_GAN.py.
• Run the program in terminal with python3 COVID_GAN.py or jupyter notebook COVID_GAN.ipynb or with any other framework of your choosing.
• The final models will be saved in a new subdirectory called "Saved_Models".

• If you would like to run the model creation/training in Google Colabs, you can make a copy of the Colab here.
• To obtain the data, go to our project resources and click Add shortcut to Drive for the whole folder.
• Ensure the hyperparameter load_data_from_Google_Drive is set to True.
• The program will define a new model and train it with the data provided and hyperparameters specified and changeable in the Colab after you've made a copy.
• Run the program on Google Colabs.
• The final models will be saved in a new directory in "My Drive" called "COVID_GAN_Saved_Models".

• Demo is located at Demo_COVID_GAN_Progression.ipynb.
• Download both the dataset and .h5 files of the final models are downloaded from project resources.
• Ensure the zipped dataset and final models are in the same directory as Demo_COVID_GAN.ipynb.
• Before running, make sure all dependencies are installed by running pip3 install -r requirements.txt.
• Ensure the hyperparameter load_data_from_Google_Drive is set to False.
• The demo will load in the pre-trained models using the .h5 files as well as the data to run the demo.
• Run the demo with jupyter notebook Demo_COVID_GAN.ipynb or with any other framework of your choosing.

• If you would like to run the demo in Google Colabs, you can make a copy of the Demo Colab here.
• To obtain the data and models, go to our project resources and click Add shortcut to Drive for the whole folder.
• Ensure the hyperparameter load_data_from_Google_Drive is set to True.
• The program will load in the pre-trained models from project resources as well as the data to run the demo.
• Run the program on Google Colabs.

Paper
Model Definition and Training Script Colab
Demo Colab
Project Resources (Final Models and Data)

[1] Age Progression/Regression by Conditional Adversarial Encoders
[2] Exploring GAN Latent Space
[3] Deep Convolutional Neural Network Tutorial
[4] GAN Tutorial
[5] Visualize Autoencoders (Latent Space)