Code from the ML4MI Boot Camp.
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AgeRegressionChallenge
FunctionFitting
ImageModalityDetector
ImageReconstruction
ImageSegmentation
ImageSynthesis
MaleFemaleRadiograph
MatlabExercises
Presentations
LICENSE
README.md
clear_and_convert_bash

README.md

ML4MI_BootCamp

Code from the UW-Madison Machine Learning for Medical Imaging (ML4MI) Boot Camp. For more information about ML4MI go to: https://www.radiology.wisc.edu/research/medical-imaging-machine-learning-initiative/

All the excercises are written in Keras which is corrently integrated into tensorflow. We use the Keras functional model which is a lot more flexible than the commonly used sequential model in examples.

Keras Documentations: https://keras.io/

Bootcamp organizers:

System Requirments

Code has been tested on a machine with a NVIDIA K80 (11gb of GPU ram). To run this you need: python 3 ( https://www.python.org/ ) tensorflow ( https://www.tensorflow.org/install/ , install tensorflow-gpu if you have one)

We installed these with the following commands.

pip install tensorflow-gpu
pip install keras
pip install matplotlib
pip install numpy
pip install livelossplot
pip install conda
pip install jupyterlab
conda install scikit-image
conda install scipy
conda install -c conda-forge --no-deps pydicom

Colab from Google Research

Some of these will run on the Google research supported Colab. This is a free cloud based enviroment supported by Google. You can click on the link in the source code or go to https://colab.research.google.com/

Working Examples:

  • FunctionFitting - Some very basic networks used for learning functions
  • ImageReconstruction - Training of an neural network to reconstruct MRI images using 1D operations
  • MaleFemaleRadiograph - Classify chest xrays as male or female
  • ImageSegmentation - Lung segmentation from CT data (need to download data yourself)

Examples missing data (work in progress):

  • AgeRegression - Regression for Age
  • ImageSynthesis - Image synthesis of brats data

Note on commits:

If you aim to push changes to this repository, please edit the jupyter notebooks and then run clear_and_convert_bash (in linux or WSL). This will convert the notebooks to python and clear the ouput.