Developer Resources for Healthcare

A Hub of News, SDKs, Technical Resources, and More for Developers Working in Healthcare.

Application Frameworks


NVIDIA Clara Imaging

NVIDIA Clara™ Imaging is an application framework that accelerates the development and deployment of AI in medical imaging. Clara Imaging offers easy-to-use, domain-optimized tools to create high-quality, labeled datasets, collaborative techniques to train robust AI models, and end-to-end software for scalable and modular AI deployments.


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NVIDIA Clara Parabricks

NVIDIA Clara™ Parabricks is a computational framework supporting genomics applications from DNA to RNA. It employs NVIDIA’s CUDA, HPC, AI, and data analytics stacks to build GPU accelerated libraries, pipelines, and reference application workflows for primary, secondary, and tertiary analysis.


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NVIDIA Clara Guardian

NVIDIA Clara™ Guardian enables AI application developers to develop and deploy smart sensors with multimodal AI anywhere in the hospital. Access healthcare specific pre-trained models, intelligent video analytics powered by NVIDIA DeepStream, and automatic speech recognition and natural language processing solutions powered by NVIDIA Jarvis conversational AI software.


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Accelerating Quantum Chemistry Simulations with AI

Learn the computational chemistry applications of machine learning covering three topics. First, use of neural networks and other machined-learned methods for describing a quantum-accurate potential energy surface. Second, graph convolution neural networks and graph message-passing networks for predicting molecular properties at a fraction of the cost of traditional electronic structure calculations. Third, the variational autoencoders for molecule discovery and illustrate their application to drug discovery.


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Clara Developer Day: Getting Started with Clara Train for High Performance & Iterative Experimentation with AutoML

Learn how using Clara Train SDK accelerates and standardizes model development for medical imaging. Learn the SDKs core concepts and capabilities to define a training workflow with the option to bring your own components. The session will also include a hands-on deep dive on how optimize hyper-parameter using AutoML.


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Clara Developer Day: Federated Learning using Clara Train SDK

Learn the core concepts of federated learning and the different collaborative learning techniques. Dive deeper into how using the Clara Train SDK enables privacy-preserving federated learning and how to easily bring up federated learning clients and establish communicated between various clients and a server for model aggregation.


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AutoML-Enabled AI Model Training

Read the latest release of Clara Train v3.0 introducing the AutoML module, making the process of hyper-parameter tuning seamless by intelligently searching for the optimal parameter settings to train models automatically.


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Deploying Healthcare AI Workflows

The Clara Deploy Application Framework allows you to ingest massive amounts of data from popular data sources like PACS (using a DICOM adapter), encapsulate your medical imaging business logic on operators (i.e. image segmentation) as part of a pipeline, and execute those jobs at scale using cloud-native paradigms based on containers and Kubernetes orchestration.


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Faster Development of Speech Models

Neural Modules is a new open source toolkit that makes it possible to easily and safely compose complex neural network architectures using reusable components. Neural Modules is also built for speed and can scale out training to multiple GPUs and multiple nodes.


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Deploying Clara Train with AWS Quick Start

Get an overview of the NVIDIA Clara Train SDK for medical imaging, and demonstrate how it can be easily deployed on the AWS Cloud using the published Quick Start.


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GPU-Accelerated Biomolecular Simulations and AI-Powered Chemistry

In this session, learn how GPU-accelerated biomolecular simulation and machine learning are being used by scientists in the fight against COVID-19.


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Building Smart Hospitals to Fight COVID-19

Learn how to use Clara Guardian to develop and deploy smart sensors with multimodal AI anywhere in a healthcare facility.


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NVIDIA Clara Train

Discuss features of the NVIDIA Clara Train application framework.


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NVIDIA Clara Deploy

Discuss features of the NVIDIA Clara Deploy application framework.


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NVIDIA Clara Parabricks

Discuss features of the NVIDIA Clara Parabricks application framework.


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Find Your Application-Specific Resource

cuDNN

The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.


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TensorRT

NVIDIA® TensorRT™ is an open-source platform for high-performance deep learning inference, which includes an inference optimizer and runtime that delivers low latency and high throughput for your healthcare applications.


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DALI

The NVIDIA Data Loading Library (NVIDIA DALI®) is a portable, open-source library for decoding and augmenting images and videos to accelerate deep learning applications in healthcare. DALI reduces latency and training time, mitigating bottlenecks, by overlapping training and pre-processing.


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TensorRT Inference Server

NVIDIA TensorRT Inference Server deploys and scales AI models in data center production. It maximizes GPU utilization and supports all major AI models and frameworks for healthcare.


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Jetpack

The NVIDIA Jetpack™ SDK is the most comprehensive solution for building AI applications. The latest release supports NVIDIA Jetson AGX Xavier™, Jetson™ TX2, Jetson TX1, and Jetson Nano.


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Automatic Mixed Precision

Deep neural network training has traditionally relied on IEEE single-precision format; however with mixed precision, you can train with half precision while maintaining the network accuracy achieved with single precision. This technique of using both single and half-precision representations is referred to as mixed-precision technique and is used by leading institutions such as Nuance.


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IndeX

NVIDIA IndeX™ is a commercial 3D volumetric visualization SDK that allows scientists and researchers to visualize and interact with massive datasets, make real-time modifications, and navigate to the most pertinent parts of the data, all in real time, to gather better insights faster.


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OptiX

The NVIDIA OptiX™ API is an application framework for achieving optimal ray-tracing performance on the GPU. It provides a simple, recursive, and flexible pipeline for accelerating ray tracing algorithms.


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NVIDIA Nsight Graphics

NVIDIA Nsight™ Graphics is a standalone developer tool that enables you to debug, profile, and export frames built with Direct3D (11, 12, DXR), Vulkan (1.1, NV Vulkan Ray Tracing Extension), OpenGL, OpenVR, and the Oculus SDK.


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GRID

NVIDIA GRID® Virtual PC (GRID vPC) and Virtual Apps (GRID vApps) are virtualization solutions that deliver a user experience that’s nearly indistinguishable from a native PC. With server-side graphics and comprehensive management and monitoring capabilities, GRID future-proofs your virtual desktop infrastructure (VDI) environment.


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vGPU

NVIDIA virtual GPU (vGPU) technology uses the power of NVIDIA GPUs and NVIDIA virtual GPU software products to offer a consistent user experience for every virtual workflow.


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NVIDIA Quadro Virtual Workstation

With workloads becoming increasingly compute-intensive and a constantly growing need for mobility and collaboration, more workloads are being migrated to the cloud. Cloud-based workstations, paired with traditional on-premises infrastructure, give an enterprise the flexibility and business agility to stay competitive.


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NGC

NGC is the hub for GPU-optimized software for deep learning, machine learning, and high-performance computing (HPC) that takes care of all the plumbing so data scientists, developers, and researchers can focus on building solutions and gathering insights.


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CUDA Toolkit

The NVIDIA CUDA Toolkit provides a development environment for creating high-performance GPU-accelerated applications. With the CUDA Toolkit, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms, and HPC supercomputers.


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RAPIDS

The RAPIDS suite of open-source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs.


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NVIDIA DEEP LEARNING INSTITUTE

The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI and accelerated computing to solve real-world problems. Training is available as self-paced, online courses or in-person, instructor-led workshops.


Data Augmentation and Segmentation with Generative Networks for Medical Imaging

A generative adversarial network (GAN) is a pair of deep neural networks: a generator that creates new examples based on the training data provided and a discriminator that attempts to distinguish between genuine and simulated data. As both networks improve together, the examples created become increasingly realistic. This technology is promising for healthcare, because it can augment smaller datasets for training of traditional networks.


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Medical Image Classification Using the MedNIST Dataset

Explore an introduction to deep learning for radiology and medical imaging by applying CNNs to classify images in a medical imaging dataset.


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NVIDIA Healthcare News


MONAI: Open-Source AI Framework for Healthcare Research

MONAI v0.2 brings new capabilities, examples, and research implementations to medical imaging researchers to accelerate the pace of innovation for AI development.


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Accelerating COVID-19 Research with GPUs

Using the Summit supercomputer, researchers at Oak Ridge National Lab's are able to screen over 1B drug compounds in 12 hours.


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