Demo minimum requirement

v0.4

• Each demo will have an independent folder directly under the 'Demos';
• Name the folder as [loader-type]_[image-modality]_[organ-disease]_[optional:brief-remark], e.g. unpaired_ultrasound_prostate or grouped_mr_brain_logitudinal;
• For simplicity and ease to convert to notebooks later, avoid sub-folders (other than those specified below) and separate files for additional functions/classes;
• Experiment using cross-validation or random splitting is NOT encouraged, unless the purpose of the demo is to demonstrate how to design experiments.

Open accessible data

• Each demo should have a 'demo_data.py' script to automatically download demo data;
• Data should be downloaded under the demo folder named dataset;
• Data should be hosted in a reliable and efficient (not stored in this repo please) online storage, Kaggle, GitHub and Zendoo are all options for non-login access (avoid google drive for known accessibility issues);
• Relevant dataset folder structure to utilise the supported loaders can be either pre-arranged in data source or scripted after downloading; Avoid slow and large data set download.

Training

• Each demo should have a 'demo_train.py' script;
• This is accompanied by a config yaml file in the same folder - please use the same folder name for the config file.

Predicting

• Each demo should have a 'demo_predict.py' script;
• A pre-trained model must be available for downloading, e.g. the same as data (not stored in this repo please);
• The pre-trained model, e.g. ckpt file, should also be downloaded under dataset folder;
• Results: Provide at least one piece of numerical metric (Dice, distance error, etc) and one piece of visualisation to show the efficacy of the registration (optimum performance is not required here).

A 'readme.md' file

The markdown file should contain the following sections:

• [Demo name] - Use the first-level subheading with # and all the following are using the second-level subheadings with ##;
• [Author] Author name and email (You can opt to be anonymous by using DeepReg Core Development Team);
• [Instruction] A step-by-step instruction how the demo can be run - preferably using the demo folder as working directory;
• [Application] Briefly describe the clinical application and the need for registration;
• [Data] Acknowledge data source.
• [Tested DeepReg Version] Demos do not need to be unit-tested. Record the version # of DeepReg that the demo has been tested.

Code style

• Please restrict using external libraries or anything unsupported by Colab or Azure;
• See general Contribution Guide.