NVIDIA today launched a cloud service for medical imaging AI to further streamline and accelerate the creation of ground-truth data and training of specialized AI models through fully managed, cloud-based application programming interfaces.
NVIDIA MONAI cloud APIs - announced at the annual meeting of RSNA, the Radiological Society of North America, taking place this week in Chicago - provide an expedited path for developers and platform providers to integrate AI into their medical imaging offerings using pretrained foundation models and AI workflows for enterprises. The APIs are built on the open-source MONAI project founded by NVIDIA and King's College London.
Medical imaging is critical across healthcare, making up approximately 90% of healthcare data. It's used by radiologists and clinicians to do screening, diagnosis and intervention, by biopharma researchers to evaluate how clinical trial patients respond to new drugs and by medical device makers to provide real-time decision support.
The scale of work across each of these areas requires a medical imaging-specific AI factory - an enterprise-grade platform that delivers large-scale data management, creates ground-truth annotations, accelerates model development and establishes seamless AI application deployment.
With NVIDIA MONAI cloud APIs, solution providers can more easily integrate AI into their medical imaging platforms, enabling them to provide supercharged tools for radiologists, researchers and clinical trial teams to build domain-specialized AI factories. The APIs are available in early access through the NVIDIA DGX Cloud AI supercomputing service.
The NVIDIA MONAI cloud API is integrated into Flywheel, a leading medical imaging data and AI platform that supports end-to-end workflows for AI development. Developers at medical image annotation companies including RedBrick AI and at machine learning operations (MLOps) platform providers including Dataiku are poised to integrate NVIDIA MONAI cloud APIs into their offerings.
Easy-to-Deploy Annotation and Training for Medical Imaging Building efficient and cost-effective AI solutions requires a robust, domain-specialized development foundation that includes full-stack optimizations for software, scalable multi-node systems and state-of-the-art research. It also requires high-quality ground-truth data - which can be arduous and time-consuming to gather, particularly for 3D medical images that require a high level of expertise to annotate.
NVIDIA MONAI cloud APIs feature interactive annotation powered by the VISTA-3D (Vision Imaging Segmentation and Annotation) foundation model. It's purpose-built for continuous learning, a capability that improves AI model performance based on user feedback and new data.
Trained on a dataset of annotated images from 3D CT scans from more than 4,000 patients, spanning various diseases and parts of the body, VISTA-3D accelerates the creation of 3D segmentation masks for medical image analysis. With continuous learning, the AI model's annotation quality improves over time.
To further accelerate AI training, this release includes APIs that make it seamless to build custom models based on MONAI pretrained models. NVIDIA MONAI cloud APIs also include Auto3DSeg, which automates hyperparameter tuning and AI model selection for a given 3D segmentation task, simplifying the model development process.
NVIDIA researchers recently won four challenges at the MICCAI medical imaging conference using Auto3DSeg. These included AI models to analyze 3D CT scans of the kidneys and heart, brain MRIs and 3D ultrasounds of the heart.
Solutions Providers, Platform Builders Embrace NVIDIA MONAI Cloud APIs Medical imaging solution providers and machine learning platforms are using NVIDIA MONAI cloud APIs to deliver critically valuable AI insights to accelerate their customers' work.
Flywheel has integrated MONAI through NVIDIA AI Enterprise and is now offering NVIDIA MONAI cloud APIs to accelerate medical image curation, labeling analysis and training. The Minneapolis-based company's centralized, cloud-based platform powers biopharma companies, life science organizations, healthcare providers and academic medical centers to identify, curate and train medical imaging data for the development of trustworthy AI.
NVIDIA MONAI cloud APIs lower the cost of building high-quality AI models for radiology, disease research and the evaluation of clinical trial data, said Dan Marcus, chief scientific officer at Flywheel. With the addition of cloud APIs for interactive annotation and automated segmentation, customers of our medical imaging AI platform can accelerate AI model development to more quickly deliver innovative solutions.
Annotation and viewer solution providers, including Redbrick AI, Radical Imaging, V7 Labs and Centaur Labs, will also use NVIDIA MONAI cloud APIs to bring AI-assisted annotation and training capabilities to market faster, without having to host and manage the AI infrastructure on their own.
RedBrick AI is integrating the VISTA-3D model available through NVIDIA MONAI cloud APIs to deliver interactive cloud annotation for its medical device customers that support distributed teams of clinicians.
VISTA-3D allows our clients to rapidly build models across different modalities and conditions, said Shivam Sharma, CEO of RedBrick AI. The foundation model is generalizable, making it easy to fine-tune for various clinical applications with accurate, reliable segmentation results.
To streamline enterprise AI model development, MLOps platform builders including Dataiku, ClearML and Weight & Biases are also investigating the use of NVIDIA MONAI cloud APIs.
Dataiku plans to integrate NVIDIA MONAI cloud APIs to further simplify AI model creation for medical imaging applications.
With NVIDIA MONAI cloud APIs, Dataiku users wo
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NVIDIA today launched a cloud service for medical imaging AI to further streamline and accelerate the creation of ground-truth data and training of specialized AI models through fully managed, cloud-based application programming interfaces.
