Under the machine-learning umbrella, you will find sub modules of code committed by individuals who are willing to share their code with SiiM. By convention, the Committee is encouraging participants to base their submissions on Juypyter (formerly iPy) notebooks to make it easy to contribute, download, run and modify.
Of course, notebooks need a runtime platform to run on. Towards that end, thanks to Dr. George Shih MD for kicking off this effort with his Docker that builds Jupyter on the deep learning framework Keras on TensorFLow. Each code contributor is asked to bundle all relevant documentation to their notebooks in their commit.
The Documentation folder is reserved for HOWTO's relevant to the entire project
- HOWTO 1: Get and run an Existing Docker (https://github.com/ImagingInformatics/machine-learning/blob/master/Documentation/HOWTO-1.md )
- HOWTO 2: Modify Existing DOckers (https://github.com/ImagingInformatics/machine-learning/blob/master/Documentation/HOWTO-2.md)
- HOWTO 3: Guidelines for code submitters (https://github.com/ImagingInformatics/machine-learning/blob/master/Documentation/HOWTO-3.md)
- HOWTO 4: Suggested public data sets for machine learning (https://github.com/ImagingInformatics/machine-learning/blob/master/Documentation/HOWTO-4.md )