oapostrophe/HeartNet

Research project developing a novel image-based method for neural network heart attack diagnosis.

HeartNet

A joint project by oapostrophe, gkenderova, soksamnanglim, syaa2018

For a high-level overview of this project, check out this blog post and 90-second demo. For a full presentation and more detailed writeup on our methodology, check out the report on our project website.

The trained model can be demoed by downloading app.py and demo_model.pkl, installing streamlit and fastai, then running:

streamlit run app.py

You can then visit the provided url in your browser; for convenience, sample generated MI and Normal EKG images are provided in the /test files directory.

To use any of the other files, you'll have to download the PTB-XL dataset.

The important files are the following:

• app.py StreamLit-based web interface using a trained model
• dataset generation/generate_imgset1.py our first iteration generating a dataset directly with MatPlotLib; these images look rough.
• dataset generation/generate_imgset2.py our second iteration that generates nicer-looking images
• dataset generation/generate_imgset3.py adds random simulated shadows overlaying generated images
• dataset generation/generate_rnn_imgset.py generates individual images for each of 12 leads, for input into an RNN (rnn code currently fails to learn).
• dataset generation/automold.py library with image augmentation code for adding shadows
• training/cnn_learner.py trains and saves a cnn on generated images.

Feel free to email me with any questions!