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

By M. Jorge Cardoso, Wenqi Li, Richard Brown, Nic Ma, E. Kerfoot, Yiheng Wang, Benjamin Murrey, Andriy Myronenko, Can Zhao, Dong Yang and 46 more. arXiv.org, 2022

996 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9b90291103892b9f9665c11461d7bc9ea40ea9ec

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Citations in the corpus

UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation (2022, methodology): “The encoder has four stages in which the number of channels at stages are [32 , 64 , 128 , 256] and each stage has three EPA blocks with the number of heads set to four.”
nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation (2024, background): “0 ) [1,28,18,32] is part of the MONAI eco-system [10] and recently created a buzz at MICCAI 2023 by winning several highly competitive challenges like KiTS2023, thereby positioning itself as an alternative to nnU-Net promising the same auto-configuration functionality [1].”