NVIDIA FLARE 2.2 includes a host of new features that reduce development time and accelerate deployment for federated learning, helping organizations cut costs for building robust AI. Get the details about what’s new in this release. An open-source platform and software development kit (SDK) for Federated Learning (FL), NVIDIA FLARE continues to evolve to enable its end users to leverage…
]]>It’s never been more important to put powerful AI tools in the hands of the world’s leading medical researchers. That’s why NVIDIA has invested in building a collaborative open-source foundation with MONAI, the Medical Open Network for AI. MONAI is fueling open innovation for medical imaging by providing tools that accelerate image annotation, train state-of-the-art deep learning models…
]]>Deep learning models have been successfully used in medical image analysis problems but they require a large, curated amount of labeled images to obtain good performance. Creating such annotations are tedious, time-consuming and typically require clinical expertise. To address this gap, Project MONAI has released MONAI Label v0.1 – an intelligent open source image labeling and learning tool…
]]>AI requires massive amounts of data. This is particularly true for industries such as healthcare. For example, training an automatic tumor diagnostic system often requires a large database in order to capture the full spectrum of possible anatomies and pathological patterns. In order to build robust AI algorithms, hospitals and medical institutions often need to collaboratively share and combine…
]]>The medical imaging industry is undergoing a dramatic transformation driven by two technology trends. Artificial Intelligence and software-defined solutions are redefining the medical imaging workflow. Deep learning research in medical imaging is booming. However, most of this research today is performed in isolation and with limited datasets. This leads to overly simplified models which only…
]]>The medical imaging industry is undergoing a dramatic transformation driven by two technology trends: Artificial Intelligence and software-defined solutions are redefining the medical imaging workflow. Artificial Intelligence, specifically deep learning, demonstrates great potential within radiology for disease detection, localization, and classification. It has already shown it can augment…
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