View More
View Less
System Message
An unknown error has occurred and your request could not be completed. Please contact support.
Wait Listed
Personal Calendar
Conference Event
Schedule TBD
Conflict Found
This session is already scheduled at another time. Would you like to...
Please enter a maximum of {0} characters.
Please enter a maximum of {0} words.
must be 50 characters or less.
must be 40 characters or less.
Session Summary
We were unable to load the map image.
This has not yet been assigned to a map.
Search Catalog
Replies ()
New Post
Microblog Thread
Post Reply
Your session timed out.
This web page is not optimized for viewing on a mobile device. Visit this site in a desktop browser to access the full set of features.
2017 GTC San Jose

L7135 - Deep Learning for Medical Image Analysis using R and MXNet (Presented by NVIDIA Deep Learning Institute)

Session Speakers
Session Description

Convolutional neural networks (CNNs) have proven to be just as effective in visual recognition tasks involving non-visible image types as regular RGB camera imagery. One important application of these capabilities is medical image analysis, where we wish to detect features indicative of medical conditions and use them to infer patient status. In addition to processing non-visible imagery, such as CT scans and MRI, these applications often require us to process higher dimensionality imagery that may be volumetric and have a temporal component. In this lab you will use the deep learning framework MXNet to train a CNN to infer the volume of the left ventricle of the human heart from a time-series of volumetric MRI data. You will learn how to extend the canonical 2D CNN to be applied to this more complex data and how to directly predict the ventricle volume rather than generating an image classification. In addition to the standard Python API, you will also see how to use MXNet through R, which is an important data science platform in the medical research community. Prerequisites: Basic knowledge of CNNs. This lab utilizes GPU resources in the cloud, you are required to bring your own laptop.

Additional Session Information
Instructor-Led Lab
AI in Healthcare Summit Deep Learning and AI
Healthcare & Life Sciences
2 Hours
Session Schedule