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Tutorial and framework code for deep learning in biomedical applications with PyTorch and (if necessary) Google Cloud Platform.

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Deep Learning in Biomedical Applications with PyTorch and Google Cloud Platform

Nanyan "Rosalie" Zhu and Chen "Raphael" Liu, Columbia University

Overview

This tutorial is designed for the course BMEN4460 Deep Learning in Biomedical Applications instructed by Dr. Andrew Laine and Dr. Jia Guo starting from the Spring 2020 semester at Columbia University. Since we are a medical imaging group, this repository will mainly focus on applications in medical imaging.

This repository is still in progress. We will add one new post per week. Please help us improve this repository by leaving your comments or problems you encountered in the "Issues" section.

GCP setup.

BMEN4460 supplementary notebooks.

  1. Simple cell segmentation with a single layered perceptron.
  2. Image classification on MNIST data.
  3. Brain tumor segmentation.
  4. GAN faking MNIST images

Tutorials

(Outline is listed. Whenever a content is complete, it will become clickable.)

Additional resources.

  1. Python Basics. This material comes from another course called Deep Learning for Computer Vision at Columbia University. Please note that we strongly recommend not to use the 'pip' install ation mentioned in that tutorial, but instead use the 'anaconda' installation we introduced here.
  2. Jupyter Notebook Tricks. Quite cool. There are a lot of things that we never heard of.
  3. Udacity Online Course. This is not free, and not relevant to the course BMEN4460 at all. It is just a course we looked into personally. We are not paid or anything to advertise this. It's just a very friendly course if you are new to deep learning.

Useful links (to be organized).

Free Online Courses

Other useful GitHubs

Papers.

Datasets

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