Healthcare - Top Resources from GTC 21

Here are the latest resources and news for healthcare developers from GTC 21, including demos and specialized sessions for building AI in drug discovery…

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NVIDIA
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Brad Nemire
•3 min read•intermediate•
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•View Original
AutoMLDeep Learning

Overview

The article presents the latest resources and news for healthcare developers from GTC 21, focusing on AI applications in drug discovery, medical imaging, genomics, and smart hospitals. It highlights new features in NVIDIA Clara Train 4.0 and provides access to on-demand sessions, demos, and blogs for developers.

What You'll Learn

1

How to use NVIDIA Clara Train 4.0 for medical imaging applications

2

Why advanced computational modeling can accelerate drug discovery

3

How to stream high-throughput medical sensor data over Ethernet

4

When to apply AutoML and Neural Architecture Search for medical imaging segmentation

5

How to enhance ATAC-seq data using deep learning toolkits

Key Questions Answered

What new features are available in NVIDIA Clara Train 4.0?
NVIDIA Clara Train 4.0 includes pre-trained models, AI-assisted annotation, AutoML, and federated learning, enhancing its application framework for medical imaging.
How can advanced computational modeling impact drug discovery?
Advanced computational modeling, when integrated with next-generation machine learning, can significantly accelerate the drug discovery process by improving efficiency and collaboration among researchers.
What technologies does NVIDIA offer for streaming medical sensor data?
NVIDIA provides Networking ConnectX NICs, Rivermax SDK with GPUDirect, and Clara AGX to facilitate efficient streaming of high-throughput medical sensor data over Ethernet.
What is the role of AutoML in medical imaging segmentation?
AutoML is used to automatically search for high-performance networks specifically designed for medical image segmentation, enhancing accuracy and efficiency in the process.
How does RAPIDS and AtacWorks enhance genomic data analysis?
RAPIDS and AtacWorks provide deep learning capabilities that enhance ATAC-seq data analysis, allowing for more accurate identification of active regulatory DNA compared to existing methods.

Technologies & Tools

Application Framework
Nvidia Clara Train 4.0
Used for medical imaging with features like pre-trained models and AI-assisted annotation.
Deep Learning Toolkit
Rapids
Enhances genomic data analysis, particularly in identifying active regulatory DNA.
Inference Server
Nvidia Triton Inference Server
Powers deep learning models for drug design.
Software Development Kit
Rivermax SDK
Facilitates streaming of medical sensor data over Ethernet.

Key Actionable Insights

1
Leverage NVIDIA Clara Train 4.0 to streamline your medical imaging projects.
By utilizing the pre-trained models and AI-assisted annotation features, developers can significantly reduce the time and effort required for model training and deployment.
2
Integrate advanced computational modeling techniques into your drug discovery workflow.
This can lead to faster identification of potential drug candidates, enhancing collaboration and efficiency in research teams.
3
Utilize NVIDIA's technologies for efficient medical sensor data streaming.
Understanding how to implement these technologies can improve the performance and reliability of medical devices in real-time applications.

Related Concepts

AI In Healthcare
Machine Learning For Medical Imaging
Deep Learning In Genomics