kaggle_ctmi

Working with DICOM images from the Kaggle CT Medical Images.

This is one of the simplest DICOM based datasets I could find. I'm taking the goal to be prediction of CT vs CTA. My goal is to create a simple clean, short solution with a minimum of added 'infrastructure'

Recent improvements

• Images and GT read from directory structure as intended
• Logging to console and log file (using standard logging library)
• Basic dockerfile, image pushed to Docker Hub
• Lazy caching of pre-processed images as .npy files
• Command-line parameters to enable/disable experiment phases (using standard argparse library).

Installation

Assuming a basic python 3.5 installation, roughly:

• clone into directory kaggle_ctmi/

• cd kaggle.ctmi

• pip install -r requirments.txt (or use a virtual environment)

• pytest - all unit tests should pass out of the box.

• Setup the 'real' 'ShaipWorkspace':

  • mkdir -p ShaipWorkspace/inputs
  • mkdir -p ShaipWorkspace/outputs/results
  • python shaip_creation/populate_shaip_directories.py

• Run the main script:

  • python kaggle_ctmi/experiment.py -tpe

• To see results point a browser at ShaipWorkspace/outputs/results/index.html

Usage

usage: experiment.py [-h] [-t] [-p] [-e]

CT/CTA discrimination to run in SHAIP

optional arguments:
  -h, --help      show this help message and exit
  -t, --train     perform model training
  -p, --predict   perform prediction over the test set
  -e, --evaluate  generate results

If no phases are specified, program does nothing - exits