Monai: An open-source framework for deep learning in healthcare

M. Jorge Cardoso; Wenqi Li; Richard Brown; Nic Ma; Eric Kerfoot; Yiheng Wang; Benjamin Murrey; Andriy Myronenko; Can Zhao; Dong Yang; Vishwesh Nath; Yufan He; Ziyue Xu; Ali Hatamizadeh; Andriy Myronenko; Wentao Zhu; Yun Liu; Mingxin Zheng; Yucheng Tang; Isaac Yang; Michael Zephyr; Behrooz Hashemian; Sachidanand Alle; Mohammad Zalbagi Darestani; Charlie Budd; Marc Modat; Tom Vercauteren; Guotai Wang; Yiwen Li; Yipeng Hu; Yunguan Fu; Benjamin Gorman; Hans Johnson; Brad Genereaux; Barbaros S. Erdal; Vikash Gupta; Andres Diaz-Pinto; Andre Dourson; Lena Maier-Hein; Paul F. Jaeger; Michael Baumgartner; Jayashree Kalpathy-Cramer; Mona Flores; Justin Kirby; Lee A. D. Cooper; Holger R. Roth; Daguang Xu; David Bericat; Ralf Floca; S. Kevin Zhou; Haris Shuaib; Keyvan Farahani; Klaus H. Maier-Hein; Stephen Aylward; Prerna Dogra; Sebastien Ourselin; Andrew Feng

Monai: An open-source framework for deep learning in healthcare

M. Jorge Cardoso, Wenqi Li, Richard Brown, Nic Ma, Eric Kerfoot, Yiheng Wang, Benjamin Murrey, Andriy Myronenko, Can Zhao, Dong Yang, Vishwesh Nath, Yufan He, Ziyue Xu, Ali Hatamizadeh, Andriy Myronenko, Wentao Zhu, Yun Liu, Mingxin Zheng, Yucheng Tang, Isaac YangMichael Zephyr, Behrooz Hashemian, Sachidanand Alle, Mohammad Zalbagi Darestani, Charlie Budd, Marc Modat, Tom Vercauteren, Guotai Wang, Yiwen Li, Yipeng Hu, Yunguan Fu, Benjamin Gorman, Hans Johnson, Brad Genereaux, Barbaros S. Erdal, Vikash Gupta, Andres Diaz-Pinto, Andre Dourson, Lena Maier-Hein, Paul F. Jaeger, Michael Baumgartner, Jayashree Kalpathy-Cramer, Mona Flores, Justin Kirby, Lee A. D. Cooper, Holger R. Roth, Daguang Xu, David Bericat, Ralf Floca, S. Kevin Zhou, Haris Shuaib, Keyvan Farahani, Klaus H. Maier-Hein, Stephen Aylward, Prerna Dogra, Sebastien Ourselin, Andrew Feng

Research output: Working paper/Preprint › Preprint

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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.

Original language	English
Publication status	Published - 4 Nov 2022

Keywords

cs.LG
cs.AI
cs.CV

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2211.02701v1

Cite this

@techreport{15be3a02480a4584953f713da42471de,

title = "Monai: An open-source framework for deep learning in healthcare",

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. ",

keywords = "cs.LG, cs.AI, cs.CV",

author = "Cardoso, {M. Jorge} and Wenqi Li and Richard Brown and Nic Ma and Eric Kerfoot and Yiheng Wang and Benjamin Murrey and Andriy Myronenko and Can Zhao and Dong Yang and Vishwesh Nath and Yufan He and Ziyue Xu and Ali Hatamizadeh and Andriy Myronenko and Wentao Zhu and Yun Liu and Mingxin Zheng and Yucheng Tang and Isaac Yang and Michael Zephyr and Behrooz Hashemian and Sachidanand Alle and Darestani, {Mohammad Zalbagi} and Charlie Budd and Marc Modat and Tom Vercauteren and Guotai Wang and Yiwen Li and Yipeng Hu and Yunguan Fu and Benjamin Gorman and Hans Johnson and Brad Genereaux and Erdal, {Barbaros S.} and Vikash Gupta and Andres Diaz-Pinto and Andre Dourson and Lena Maier-Hein and Jaeger, {Paul F.} and Michael Baumgartner and Jayashree Kalpathy-Cramer and Mona Flores and Justin Kirby and Cooper, {Lee A. D.} and Roth, {Holger R.} and Daguang Xu and David Bericat and Ralf Floca and Zhou, {S. Kevin} and Haris Shuaib and Keyvan Farahani and Maier-Hein, {Klaus H.} and Stephen Aylward and Prerna Dogra and Sebastien Ourselin and Andrew Feng",

note = "www.monai.io",

year = "2022",

month = nov,

day = "4",

language = "English",

type = "WorkingPaper",

}

Cardoso, MJ , Li, W , Brown, R, Ma, N, Kerfoot, E, Wang, Y, Murrey, B, Myronenko, A, Zhao, C, Yang, D, Nath, V, He, Y, Xu, Z, Hatamizadeh, A, Myronenko, A, Zhu, W, Liu, Y, Zheng, M, Tang, Y, Yang, I, Zephyr, M, Hashemian, B, Alle, S, Darestani, MZ, Budd, C , Modat, M , Vercauteren, T , Wang, G, Li, Y, Hu, Y, Fu, Y, Gorman, B, Johnson, H, Genereaux, B, Erdal, BS, Gupta, V, Diaz-Pinto, A, Dourson, A, Maier-Hein, L, Jaeger, PF, Baumgartner, M, Kalpathy-Cramer, J, Flores, M, Kirby, J, Cooper, LAD, Roth, HR, Xu, D, Bericat, D, Floca, R, Zhou, SK, Shuaib, H, Farahani, K, Maier-Hein, KH, Aylward, S, Dogra, P, Ourselin, S & Feng, A 2022 'Monai: An open-source framework for deep learning in healthcare'.

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T1 - Monai

T2 - An open-source framework for deep learning in healthcare

AU - Cardoso, M. Jorge

AU - Li, Wenqi

AU - Brown, Richard

AU - Ma, Nic

AU - Kerfoot, Eric

AU - Wang, Yiheng

AU - Murrey, Benjamin

AU - Myronenko, Andriy

AU - Zhao, Can

AU - Yang, Dong

AU - Nath, Vishwesh

AU - He, Yufan

AU - Xu, Ziyue

AU - Hatamizadeh, Ali

AU - Myronenko, Andriy

AU - Zhu, Wentao

AU - Liu, Yun

AU - Zheng, Mingxin

AU - Tang, Yucheng

AU - Yang, Isaac

AU - Zephyr, Michael

AU - Hashemian, Behrooz

AU - Alle, Sachidanand

AU - Darestani, Mohammad Zalbagi

AU - Budd, Charlie

AU - Modat, Marc

AU - Vercauteren, Tom

AU - Wang, Guotai

AU - Li, Yiwen

AU - Hu, Yipeng

AU - Fu, Yunguan

AU - Gorman, Benjamin

AU - Johnson, Hans

AU - Genereaux, Brad

AU - Erdal, Barbaros S.

AU - Gupta, Vikash

AU - Diaz-Pinto, Andres

AU - Dourson, Andre

AU - Maier-Hein, Lena

AU - Jaeger, Paul F.

AU - Baumgartner, Michael

AU - Kalpathy-Cramer, Jayashree

AU - Flores, Mona

AU - Kirby, Justin

AU - Cooper, Lee A. D.

AU - Roth, Holger R.

AU - Xu, Daguang

AU - Bericat, David

AU - Floca, Ralf

AU - Zhou, S. Kevin

AU - Shuaib, Haris

AU - Farahani, Keyvan

AU - Maier-Hein, Klaus H.

AU - Aylward, Stephen

AU - Dogra, Prerna

AU - Ourselin, Sebastien

AU - Feng, Andrew

N1 - www.monai.io

PY - 2022/11/4

Y1 - 2022/11/4

N2 - 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.

AB - 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.

KW - cs.LG

KW - cs.AI

KW - cs.CV

M3 - Preprint

BT - Monai

ER -

Monai: An open-source framework for deep learning in healthcare

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Keywords

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