We welcome all contributions that help us achieve our aim of speeding up Machine Learning (ML) / AI research in health and life sciences.
Examples of contributions are
- Data loaders for specific health & life sciences data
- Network architectures and components for deep learning models
- Tools to analyze and/or visualize data
- Bug reports
- Documentation improvements, including fixing typos
- Suggestions about codebase improvements
All contributions to the toolbox need to come with unit tests, and will be reviewed when a pull request is started.
If in doubt, reach out to the core hi-ml
team before starting your work.
Please look through the existing folder structure to find a good home for your contribution.
For a full set of design and guidelines, please check our coding guidelines documentation.