Clara Medical Imaging

Clara Medical Imaging is a collection of developer toolkits built on NVIDIA’s compute platform for accelerating compute, AI, and advanced visualization. The latest release provides the tools, APIs, and development framework needed to train and deploy AI workflows.

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Clara Genomics

Clara Genomics addresses the growing size and complexity of genomics sequencing and analysis with accelerated and intelligent computing. The Clara Genomics Analysis SDK is now available under open-source terms.


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Clara Embedded Devices

The NVIDIA Clara AGX SDK enables medical devices that need the ability to perform real-time AI and advanced image, video, and signal processing.




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AI

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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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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Annotate, Build, and Adapt Models for Medical Imaging

The Clara Train SDK, part of the Clara AI toolkit, gives data scientists and developers the tools to accelerate data annotation, development, and adaptation of AI algorithms for medical imaging.



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Build, Manage, and Deploy AI-Enhanced Clinical Workflows

The Clara Deploy SDK provides the framework and tools required to define an application workflow that can be integrated and executed once a neural network is available.


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Clara AI ToolKit

In this webinar, data scientists and developers will receive a walk-through of reference applications and learn about the latest features of the NVIDIA Clara AI toolkit and how they can get engaged with Clara Train and Clara Deploy SDKs.


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Cryo-EM in Drug Discovery

This webinar will focus on highlighting the computational challenges for cryogenic electron microscopy (cryo-EM), solutions and implementations in the powerful cryoSPARC software suite, and the application of cryo-EM within the structure-based drug design pipeline at Merck.

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Data Science in Healthcare

Learn how RAPIDS, a new open-source project, can speed up your data science workflows by bringing the power of GPU acceleration to your end-to-end machine learning (ML) pipeline for healthcare applications.



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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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Clara Train SDK

Forum for discussing the AI-Assisted Annotation SDK.

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Clara Deploy SDK

Open development platform for medical imaging.

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Clara Genomics SDK

Forum for discussing the Clara Genomics SDK.

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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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Modeling Time-Series Data with Recurrent Neural Networks in Keras

Recurrent neural networks (RNNs) allow models to classify or forecast time-series data, like natural language, markets, and even a patient’s health over time.






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NVIDIA Healthcare News


NVIDIA Clara for Clinical Deployment at the Ohio State University

To integrate a research model into a radiologist’s clinical workflow, Ohio State University researchers worked alongside an NVIDIA team to deploy their model in a clinical setting with NVIDIA Clara Deploy SDK



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NVIDIA and NIH Researchers Develop an AI Tool with Clara Train SDK to Better Detect Prostate Cancer

To help improve diagnostic and treatment accuracy for prostate cancer patients, NVIDIA scientists partnered with the National Institutes of Health (NIH) to develop a new segmentation model based on a domain generalization method through Clara Train SDK.


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