Credits: S. Dahan, LZJ. Williams

This repository provides a reference Docker algorithm container for a SLCN 2022 Challenge submission, on the grand-challenge plateform.

It should serve as an example or/and a template for your own algorithm container implementaion.

Here, a Surface Vision Transformer (SiT) model is used for the task of birth age prediction as an example. Code is based on this Github.

More information about algorithm container and submission can be found here.

1. Prerequisites
2. Overview of the project structure
3. Requirements for Grand Challenge submission (input/output)
4. Tips and general advice
5. Contacts
6. Acknowledgements

Submissions are based on Docker containers and the evalutils library (provided by Grand-Challenge).

First, you will need to install localy Docker.

Then, you will need to install evalutils, that you can pip install:

pip install evalutils

Optional: To have GPU support for local testing, you want to install the NVIDIA container toolkit.

The structure of this repository is based on the Algorithm Container for Classification in evalutils.

You can either start a project from scratch by following guidelines in evalutils documentation or by clonign this repository:

git clone https://github.com/metrics-lab/SLCN_challenge

Remark: As evalutils does not implement a class for Regression problem, we only adapted the Classification class to the case of regression problems.

No matter what methods you used to start your project (evalutils or cloning this repo), you should have at least the following files in your project repository:

.
└── slcn_project
    ├── Dockerfile               # Defines how to build your algorithm container    
    ├── build.sh                 # Builds your algorithm container
    ├── test.sh                  # A script that runs your algorithm container using the example in ./test 
    ├── .gitignore               # Define which files git should ignore (optional)
    ├── process.py               # Contains your algorithm code - this is where you will extend the BaseAlgorithm class
    ├── README.md                # For describing your algorithm to others
    ├── requirements.txt         # The python dependencies of your algorithm container - add any new 
    ├── test                     # A folder that contains an example test image for testing
    │   ├── <uid>.mha            # An example test image
    │   └── expected_output.json # Output file expected to be produced by the algorithm container

You Docker container (via process.py) is supposed to read .mha image files.

Important: Images will be read successively and predictions will be made one by one, ie there will be one birth-age.json file per predicition.