A guide to deep learning in healthcare

Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun & Jeff Dean

Nature Medicine
volume 25, pages 24–29 (2019)

Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.

https://www.nature.com/articles/s41591-018-0316-z

Graph Wavelet Neural Network (ICLR 2019) -- Pytorch implementation
Paper: https://openreview.net/forum?id=H1ewdiR5tQ

PyTorch: https://github.com/benedekrozemberczki/GraphWaveletNeuralNetwork