NVIDIA Clara is a collection of Healthcare specific developer tools built on NVIDIA’s compute platform aimed at accelerating data acquisition, analysis, and data integration.
Throughout the healthcare industry, the number of software defined solutions has grown exponentially, creating efficiencies through automation and generating massive amounts of digital data. Artificial intelligence has allowed for this data to be integrated and analyzed in new ways, generating deeper insights. NVIDIA Clara aims at providing access to technological advancements in hardware and software for developers across medical imaging and genomics to accelerate the future of medicine.
From automating workflows to improving processing speed and image quality, medical imaging developers around the world are discovering numerous ways to use AI to assist doctors in detecting and diagnosing disease. With the Clara platform, they are harnessing AI to transform healthcare workloads.
Clara Medical Imaging is a collection of developer toolkits built on NVIDIA’s compute platform aimed at accelerating compute, artificial intelligence, and advanced visualization. Medical imaging industry is being transformed. A decade ago, the earliest applications to take advantage of GPU computing were image & signal processing applications. Today, GPUs are found in almost all imaging modalities, including CT ,MRI/MRT ,X-ray and Ultrasound bringing more compute capabilities to the edge devices. Deep Learning research in Medical Imaging is also booming with more efficient and improved approaches being developed to enable AI-assisted workflows.Today, most of this AI research is being done in isolation and with limited datasets which may lead to overly simplified models. Even when a fully validated model is available, it is a challenge to deploy the algorithm in a local environment. With the latest release of Clara AI for Medical Imaging now data scientists, researchers and software developers have the necessary tools, APIs and development framework to train and deploy AI workflows.
NVIDIA Clara AI technology stack includes systems software libraries that form the foundation of GPU computing and abstracted software tools, containers, and workflow defining pipelines that allow data scientist and medical imaging developers to build and deploy AI for clinical workflows as well as accelerated research in Medical Imaging.
Deep Learning Libraries
The compute Foundation of Clara platform is based off CUDA acceleration and System Software libraries for compute and visualization that expose capabilities of GPUs through SDKs and low level APIs. The compute Foundation of Clara platform is based off CUDA acceleration and System Software libraries for compute and visualization that expose capabilities of GPUs through SDKs and low level APIs.
The field of Genomics has several transformative trends that put computing at the forefront of progress: increasing instrument throughput, AI enabled analysis applications and reduction in cost of sequencing to study large populations.
NVIDIA’s GPU Accelerated Computing platform enables real-time genomics workflows with high performance computing, deep learning and analytics on a single architecture that lives on the edge in the sequencer to the datacenter and every public cloud.
A high-level workflow from sample prep to final analysis that starts with isolating the DNA of an organism. This isolated sample is then loaded on a sequencing instruments, where embedded GPUs are used to accelerate primary analysis and enable next-generation base calling using deep neural networks (DNNs).
Secondary analysis or sequence analysis uses NVIDIA GPU computing for the Genome Analysis Toolkit (GATK), DNN-based variant calling, and de novo genome assembly.
Our first release of Clara Genomics SDK will be focused on de novo assembly of long read sequencing from Oxford Nanopore and Pacific Biosciences, reducing analysis time from days to hours. The initial release includes GPU accelerated libraries and GPU accelerated applications.
Clara Genomics Technology Stack
Clara Genomics Technology Stack includes CUDA accelerated software system libraries that form the foundation of GPU computing.
CUDA Mapper - CUDA based library enabling algorithms for overlapping sequencing reads.
CUDA Aligner - CUDA accelerated library including algorithms for aligning sequencing reads, used for genome assembly applications such as Racon and for variant calling.
CUDA POA - CUDA library for accelerated partial order alignment, used for genome assembly polishing with applications such as Racon.
These system libraries form the compute foundation and enable the GPU acceleration of the following applications:
Racon Polisher - An extension of the open source Racon consensus module for genome assembly that utilizes cudaPoa for accelerated partial order alignment.
Racon Aligner and Mapper - Will be available in upcoming releases.
Clara Train SDK enables data scientists and medical researchers with state of the art tools and technologies that accelerate data annotation, adaptation and development of AI models for Medical Imaging workflows.
Key Capabilities of Clara Train SDK include:
APIs to add AI-assisted annotation to any medical viewer with new features like Auto-Annotation and interactive annotation modes, Annotation Server which makes pre-trained models available to the client application and client APIs hosted on Github that make integration with your Medical viewer application seamless.These capabilities are already integrated into the latest MITK workbench plugin.
The SDK provides capabilities to use techniques like Transfer learning to adapt or train deep learning models from scratch, enabling Data Scientists to bring their own model architectures and run workflows, this is made possible through a unified foundation of Medical Model Archive (MMAR)
The MMAR (Medical Model Archive) provides a model development environment; defines a standard structure for storing and organizing all artifacts produced during the model development life cycle.
MMAR includes NVIDIA pretrained models based on AH-Net, DenseNet, ResNet, Dextr3D packaged as complete 2D/3D model applications for organ based segmentation, classification and annotation.
Key Features of Clara Train SDK 1.1
Bring Your Own Models, Transforms, Readers, Losses and Metrics - Learn More
Configurable framework to simplify deep learning tasks from medical images.
Medical Model Archives (MMAR) including deep learning models and artifacts.
Model adaptation and retraining that is easy to use in heterogeneous multiple GPU environments.
Model Export API for easier deployment of applications to TensorRT based inference.
"We were able to get our hands on NVIDIA’s AI Assisted Annotation technology and integrate it into our viewer in a couple of days’ time. We currently annotate a lot of images - sometimes on the order of 1000 or more a day, so any technology that can help automate this process could potentially have a significant impact in reducing the time and cost of annotation. We are excited to leverage the AI assisted workflows and work with NVIDIA to solve these critical medical imaging problems."
— Mark Michalski, Executive Director at MGH & BWH Center for Clinical Data Science
Clara Deploy SDK
Clara Deploy SDK provides a container based development & deployment framework for building AI accelerated medical imaging workflows, it uses Kubernetes under the hood and enables developers and data scientists to define a multi-staged container based pipeline.The modular architecture allows developers to use the offerings of the platform end-end or customize the workflow pipelines with bring-your-own algorithms.
The capabilities forming the Clara Deploy SDK include:
Data Ingestion interface to communicate to Hospital PACs system
Cores services for orchestrating and managing resources for workflow deployment and development
Reference AI applications that can be used as-is with user defined data or can be modified with user-defined-AI algorithms
Lastly, Clara Deploy framework also includes Visualization capabilities to monitor progress and view final results
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