Preprint
MONAI: An open-source framework for deep learning in healthcare
ArXiv.org
11/04/2022
DOI: 10.48550/arXiv.2211.02701
Abstract
Artificial Intelligence (AI) is having a tremendous impact across most areas
of science. Applications of AI in healthcare have the potential to improve our
ability to detect, diagnose, prognose, and intervene on human disease. For AI
models to be used clinically, they need to be made safe, reproducible and
robust, and the underlying software framework must be aware of the
particularities (e.g. geometry, physiology, physics) of medical data being
processed. This work introduces MONAI, a freely available, community-supported,
and consortium-led PyTorch-based framework for deep learning in healthcare.
MONAI extends PyTorch to support medical data, with a particular focus on
imaging, and provide purpose-specific AI model architectures, transformations
and utilities that streamline the development and deployment of medical AI
models. MONAI follows best practices for software-development, providing an
easy-to-use, robust, well-documented, and well-tested software framework. MONAI
preserves the simple, additive, and compositional approach of its underlying
PyTorch libraries. MONAI is being used by and receiving contributions from
research, clinical and industrial teams from around the world, who are pursuing
applications spanning nearly every aspect of healthcare.
Details
- Title: Subtitle
- MONAI: An open-source framework for deep learning in healthcare
- Creators
- M. Jorge CardosoWenqi LiRichard BrownNic MaEric KerfootYiheng WangBenjamin MurreyAndriy MyronenkoCan ZhaoDong YangVishwesh NathYufan HeZiyue XuAli HatamizadehWentao ZhuYun LiuMingxin ZhengYucheng TangIsaac YangMichael ZephyrBehrooz HashemianSachidanand AlleMohammad Zalbagi DarestaniCharlie BuddMarc ModatTom VercauterenGuotai WangYiwen LiYipeng HuYunguan FuBenjamin GormanHans JohnsonBrad GenereauxBarbaros S ErdalVikash GuptaAndres Diaz-PintoAndre DoursonLena Maier-HeinPaul F JaegerMichael BaumgartnerJayashree Kalpathy-CramerMona FloresJustin KirbyLee A. D CooperHolger R RothDaguang XuDavid BericatRalf FlocaS. Kevin ZhouHaris ShuaibKeyvan FarahaniKlaus H Maier-HeinStephen AylwardPrerna DograSebastien OurselinAndrew Feng
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arXiv.2211.02701
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 11/04/2022
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry; The Iowa Institute for Biomedical Imaging; The Iowa Initiative for Artificial Intelligence; Iowa Informatics Initiative
- Record Identifier
- 9984310139502771
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