NVIDIA MONAI is an enterprise-grade solution built on the open-source MONAI framework, offering advanced tools and support for AI development in medical imaging. The NVIDIA MONAI Toolkit provides a comprehensive sandbox environment, accelerating the creation, training, and deployment of AI models for healthcare applications.
Project MONAI
Project MONAI (Medical Open Network for AI) is a collaborative initiative co-founded by NVIDIA and leading academic medical centers. This open-source framework aims to:
Foster an inclusive community of AI researchers in healthcare imaging
Develop and share best practices across academia and industry
Accelerate AI advancements and their clinical applications
As a domain-specific Medical AI framework, MONAI:
Drives research breakthroughs
Facilitates the transition of AI into clinical practice
Harnesses medical data for deep learning model development
Offers essential tools for the entire medical AI lifecycle, from data labeling to model training
MONAI’s comprehensive toolkit enables researchers and developers to easily create, reproduce, and standardize medical AI workflows. The open-source version of MONAI provides accelerated tools and high-throughput workflows, as well as community forum support through the MONAI Issues GitHub.
NVIDIA MONAI
NVIDIA MONAI is NVIDIA’s commercial solution for organizations seeking to leverage MONAI in an enterprise environment. It builds upon the open-source MONAI framework, offering additional features and support tailored for commercial applications. Key features include:
Requires an NVIDIA AI Enterprise (NVAIE) 5.1 license
Provides access to the NVIDIA MONAI Toolkit container
Designed for enterprise developers and researchers
Offers a secure and scalable workflow for medical imaging AI development
In addition to the features available in the open-source version, NVIDIA MONAI provides:
Full-stack and business-critical support for multiple deployment options, including bare-metal, containerized, and cloud environments
Access to NVIDIA experts for guidance on configuration and performance optimization
Enterprise training services, including instructor-led workshops and self-paced training for developers, data scientists, and IT professionals
Priority security notifications for the latest fixes and maintenance releases
Support for other NVIDIA software and SDKs, including RAPIDS, TensorRT, and more
This enterprise-grade offering enhances MONAI’s capabilities, providing a comprehensive solution for organizations looking to implement AI in medical imaging at scale. It combines the power of the open-source framework with the reliability and support needed for commercial applications, making it an ideal choice for healthcare institutions and research facilities aiming to leverage AI in their clinical workflows.
NVIDIA MONAI Toolkit
NVIDIA MONAI Toolkit is a comprehensive development sandbox offered as part of NVIDIA MONAI, an NVIDIA AI Enterprise-supported distribution of MONAI. Building upon the open-source MONAI framework, it provides enhanced features and enterprise-grade support tailored for commercial applications.
The toolkit includes a base container and a curated library of 15 pre-trained models covering a wide range of medical imaging modalities including CT, MR, Pathology, and Endoscopy. Available on NGC, this toolkit empowers data scientists and clinical researchers to accelerate their AI development in medical imaging.
Key Features
NVIDIA MONAI Toolkit significantly accelerates training time, reducing it from weeks or months to just days. It enables federated learning across various platforms and provides seamless integration with existing PyTorch workflows. The toolkit offers domain-specialized tools specifically designed for medical imaging AI development, making it an invaluable resource for professionals in this field.
Key features include:
Standardized AI model development for reproducibility and collaboration
Scalability to handle large-scale medical imaging datasets
Interoperability with various medical imaging formats and clinical workflows
Continuous updates to ensure access to cutting-edge tools and techniques
Federated learning support for collaborative research while maintaining data privacy
Components
MONAI Label
MONAI Label is an intelligent labeling and learning tool that leverages active learning to reduce data labeling costs by up to 75%. It integrates seamlessly with popular open-source viewers like 3D Slicer, OHIF, QuPath, Digital Slide Archive, and CVAT, as well as cloud service providers. Developers can also incorporate MONAI Label into custom viewers using well-documented server and client APIs.
The active learning capabilities of MONAI Label are particularly noteworthy. This process aims to use the least amount of data to achieve the highest possible model performance. By focusing human annotators on the most impactful data points, MONAI Label significantly increases labeling and training efficiency while improving overall model performance.
MONAI Core
MONAI Core is a PyTorch-driven library specifically designed for deep learning tasks in medical imaging. It offers domain-optimized capabilities crucial for developing medical imaging training workflows. Key features include:
Smart Caching: Optimizes data loading and processing for faster training iterations
GPU-accelerated I/O: Leverages GPU power for faster data input/output operations
Optimized transforms: Provides efficient data augmentation and preprocessing techniques
These features significantly reduce training times from days to hours, or even minutes, greatly enhancing researcher productivity and throughput.
The Auto3DSeg feature allows developers to train 3D segmentation models with just 1-5 lines of code, cutting training time from weeks or months to merely 2 days. This dramatic reduction in development time allows for faster iteration and experimentation.
MONAI Core also supports federated learning, with client algorithm APIs that can integrate with platforms like NVIDIA FLARE. This enables collaborative learning across institutions while maintaining data privacy, a crucial feature in medical AI research.
MONAI Model Zoo
The MONAI Model Zoo offers a curated library of 25 pre-trained models (15 by NVIDIA) across various medical imaging domains. This expanded collection covers a broader range of applications, allowing data scientists and clinical researchers to jumpstart their AI development by leveraging existing, well-tuned models. These models can serve as excellent starting points for transfer learning or as benchmarks for new model development.
Onboarding Resources
To ease the onboarding process, NVIDIA MONAI Toolkit includes curated Jupyter notebooks and comprehensive tutorial resources. These materials help users quickly familiarize themselves with the toolkit’s capabilities and best practices. The notebooks cover a range of topics from basic usage to advanced techniques, ensuring that users at all levels can benefit from the toolkit.
Target Users
Researchers: Deep learning researchers can use MONAI to build novel domain-specific models and techniques, driving AI-powered acceleration for workflows or products.
Data Scientists: MONAI aids data scientists in adapting existing state-of-the-art domain techniques for specific data and use cases, refining and fine-tuning data science pipelines to fit clinical workflows or applications.
Application Developers: Platform developers can leverage MONAI components to build comprehensive solutions, such as labeling platforms using MONAI Label or complete ML platforms integrated with data stores for efficient data flow management.
IT Admins: IT administrators can easily provision and configure systems to deploy the MONAI Toolkit sandbox, serving enterprise data science and research teams with a robust, ready-to-use environment.
Additional Resources
For more information and to get started with NVIDIA MONAI, please visit the following resources:
For technical support or to learn more about licensing options, please contact NVIDIA Enterprise Support.