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Copyright © German Cancer Research Center (DKFZ), Division of Medical Image Computing (MIC). Please make sure that your usage of this code is in compliance with the code license: License

MOOD 2020 - Repository

This repo has the supplementary code for the Medical Out-of-Distribution Analysis Challenge at MICCAI 2020.

Also checkout our Website and Submission Platform.

Requirements

Please install and use docker for submission: https://www.docker.com/get-started

For GPU support you may need to install the NVIDIA Container Toolkit: https://github.com/NVIDIA/nvidia-docker

Install python requirements:

pip install -r requirements.txt

We suggest the following folder structure (to work with our examples):

data/
--- brain/
------ brain_train/
------ toy/
------ toy_label/
--- colon/
------ colon_train/
------ toy/
------ toy_label/

Run Simple Example

Have a lot at the simple_example in how to build a simple docker, load and write files, and run a simple evaluation. After installing the requirements you can also try the simple_example:

python docker_example/run_example.py -i /data/brain/ --no_gpu False

With -i you can pass an input folder (which has to contain a toy and toy_label directory) and with --no_gpu you can turn on/off GPU support for the docker (you may need to install the NVIDIA Container Toolkit for docker GPU support).

Test Your Docker

After you built your docker you can test you docker locally using the toy cases. After submitting your docker, we will also report the toy-test scores on the toy examples back to you, so you can check if your submission was successful and the scores match:

python scripts/test_docker.py -d mood_docker -i /data/ -t sample

With -i you can pass the name of your docker image, with -i pass the path to your basedata dir (see _Requirements), with -t you can define the Challenge Task (either sample or pixel), and with --no_gpu you can turn on/off GPU support for the docker (you may need to install the NVIDIA Container Toolkit for docker GPU support).

Scripts

In the scripts folder you can find:

  • test_docker.py : The script to test your docker.
  • evalresults.py : The script with our evaluation code.

Example Algorithms

For 'ready to run' simple example algorithms checkout the example_algos folder.