This document identifies key concepts of project MONAI at a high level, the goal is to facilitate further technical discussions of requirements,roadmap, feasibility and trade-offs.
Vision
Develop a community of academic, industrial and clinical researchers collaborating and working on a common foundation of standardized tools.
Create a state-of-the-art, end-to-end training toolkit for healthcare imaging.
Provide academic and industrial researchers with the optimized and standardized way to create and evaluate models
Targeted users
Primarily focused on the healthcare researchers who develop DL models for medical imaging
Goals
Deliver domain-specific workflow capabilities
Address the end-end “Pain points” when creating medical imaging deep learning workflows.
Provide a robust foundation with a performance optimized system software stack that allows researchers to focus on the research and not worry about software development principles.
Guiding principles
Modularity
Pythonic -- object oriented components
Compositional -- can combine components to create workflows
Extensible -- easy to create new components and extend existing components
Easy to debug -- loosely coupled, easy to follow code (e.g. in eager or graph mode)
Flexible -- interfaces for easy integration of external modules
User friendly
Portable -- use components/workflows via Python “import”
Run well-known baseline workflows in a few commands
Access to the well-known public datasets in a few lines of code
Standardisation
Unified/consistent component APIs with documentation specifications
Unified/consistent data and model formats, compatible with other existing standards