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Welcome to QTIM Tutorials!

The purpose of these tutorials is to provide a broad overview of techniques for applying machine learning to medical images.

We focus on building a set of skills to make you an effective machine learning practitioner with a good understanding of the underlying theory:

  • Python programming: variables, data types, data structures, control flow, I/O
  • Project management: data organization, modular code design, version control
  • Data science: tabular data, feature selection, normalization, plotting
  • Image processing: multi-dimensional arrays, thresholding, histograms, morphological operations
  • Machine learning models: decision trees, random forests, support vector machines, logistic regression, multi-layer perceptrons
  • Deep learning: convolutional neural networks, image classification, image segmentation, feature visualization

Requirements

The tutorials are designed such that anyone with an interest in applying ML to medical images can follow along, regardless of programming experience. However, the following prior experience would be useful:

  • Rudimentary programming skills in a high-level language, such as Python or MATLAB
  • Using the terminal to navigate your computer's file system and run simple commands
  • A basic understanding of linear algebra, particularly operations between scalars, vectors and matrices
  • Familiarity with basic image processing techniques

Getting started

All of our tutorials are written in Python 3 as Jupyter Notebooks. To get started, follow the installation steps below:

  1. Download and install Python 3.6 (or greater). We highly recommend Anaconda, which includes many of the dependencies you'll need (including Jupyter). The process will vary depending on platform, but is usually straightfoward.
  2. Download the tutorial notebooks from here. We highly recommend using Git, as the tutorials will be updated as time goes on. If you're unfamilar with git, just download and unpack the code as a zip file.
  3. In a terminal, install the following additional dependencies using pip (comes with Anaconda):
    • pip install tensorflow
    • pip install keras

Running a tutorial

  1. In a terminal, navigate to the qtim_Tutorials folder you cloned/unzipped in step 2 above.
  2. Run the command jupyter notebook. A new browser window/tab should appear.
  3. Click on the tutorial folder you want to run, and then click the .ipynb file in that folder.
  4. To run a chunk of code, click in a box and hit Ctrl + Enter (or the 'Run' button in the top panel).

Troubleshooting

If you happen to hit some errors, then consider the following:

  • Code blocks often rely on each other, and must be run in order (top to bottom). Be sure to go back and check all preceding blocks have been run.
  • The [*] beside a code block indicates that the code is still running. You'll need to wait until it finishes before moving on.
  • Be sure to read the descriptions as you go; some steps involve downloading external data.
  • File paths can be confusing for first-time coders. The easiest solution is to put any downloaded data in the same folder as the tutorial you're running, that way you can reference the data by folder name, rather than /lots/of/nested/directories/...
  • If all else fails, Stack Overflow is your friend :)

Contact (@mgh.harvard.edu)

James Brown: jbrown97, Andrew Beers: abeers