Radiologists carry a lot of responsibility, specializing in an area of medicine that is crucial in determining an accurate diagnosis. AI-powered tools can help simplify the lives of radiologists, accelerating workflows and providing a second set of eyes with less bias. Segmenting lesions, calculating blood flow, detecting nodules, and triaging biopsies are just some of the benefits that AI brings to medical imaging.
Using NVIDIA DGX™ A100 to accelerate AI model training, researchers at Mass General’s Athinoula A. Martinos Center for Biomedical Imaging are working on models to segment and align multiple chest scans to calculate lung disease severity.
MONAI is an open-source, PyTorch-based, domain-optimized AI framework for medical imaging with over 20 pre-trained models that has been adopted by leading healthcare research institutions.
Qure.AI has been developing AI tools to detect signs of disease from lung scans since 2016. When COVID-19 began to spread, they raced to retool their solution to address clinicians' urgent needs.
India accounts for over 25 percent of the 10 million tuberculosis cases each year. To help detect the disease, DeepTek has developed an AI tool that has been used on over 70,000 chest x-rays in India to date.
Using artificial intelligence, the Sydney Neuroimaging Analysis Centre (SNAC) saw a 15X speedup in segmentation tasks. Whole-brain extraction from MRI images once took 20-30 minutes and now are done in 2-3 minutes.
Browse on-demand medical imaging sessions at GTC and learn the latest from academic medical research centers and developers around the world in applying AI to medical imaging.
Learn how XNAT and NVIDIA Clara™ Imaging lower the barriers to AI adoption by streamlining end-to-end lifecycle management of data, AI models, and deployment workflows.
Learn the benefits and steps needed to be integrated with medical imaging AI marketplaces and the process for gaining approval for your AI algorithm.
Power AI-enabled instruments and AI-enhanced medical imaging workflows using NVIDIA Clara Imaging. Developers can get started with domain-specific application frameworks to kickstart the creation of AI applications and seamlessly integrate them into clinical workflows at scale.
Whether building and managing embedded devices at the edge or AI infrastructure to support compute-intensive workloads, NVIDIA offers solutions to develop and deploy optimized and scalable applications for medical imaging workflows, including AI training, image reconstruction, and image processing.
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