NIH VideoCast - Overview of Deep Learning in Healthcare

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Overview of Deep Learning in Healthcare

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Air date:	Thursday, September 8, 2016, 3:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 844 (458 Live, 386 On-demand)
Category:	Special
Runtime:	01:26:24
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	Data Science at NIH The Office of the Associate Director for Data Science (ADDS) announces training opportunities in Deep Learning, a statistical/machine learning classification technique. Although computationally demanding, recently it has been used successfully to model complex nonlinear functions such as images. The first part of this two-part series will be held on September 8th. It will be an hour-long overview followed by a half hour for questions. The second part will be a day-long, in-depth workshop held on September 22. Attending or watching the overview first is highly recommended. Machine Learning (ML) has become a core technology underlying many modern applications, especially in healthcare. Machine learning techniques provide powerful methods for analyzing large data sets, such as medical images, electronic health records, and genomics. Recent advances in Deep Learning (DL) provide an analysis framework that can be used to automatically classify images and objects with (and occasionally exceeding) human-level accuracy. A key advantage of Deep Learning is its ability to perform unsupervised feature extraction over massive data sets making big data part of the solution -- not the problem. Deep Learning is rapidly becoming a key tool at many of the top technology companies around the world. This talk will introduce DL in the broader context of machine learning and discuss critical factors driving the success of DL with examples of how deep learning is advancing healthcare. We will also outline development and deployment workflows for building powerful DL solutions and provide an overview of relevant open source tool kits, companies, and products. We will wrap up with a short demo of NVIDIA’s DIGITS training system for rapidly prototyping your own deep learning applications.

NLM Title:	Overview of deep learning in healthcare / Abdul Al Halabi, Abel Brown.
Author:	Al Halabi, Abdul. Brown, Abel. Data Science at NIH (Initiative)
Publisher:
Abstract:	(CIT): Data Science at NIH The Office of the Associate Director for Data Science (ADDS) announces training opportunities in Deep Learning, a statistical/machine learning classification technique. Although computationally demanding, recently it has been used successfully to model complex nonlinear functions such as images. The first part of this two-part series will be held on September 8th. It will be an hour-long overview followed by a half hour for questions. The second part will be a day-long, in-depth workshop held on September 22. Attending or watching the overview first is highly recommended. Machine Learning (ML) has become a core technology underlying many modern applications, especially in healthcare. Machine learning techniques provide powerful methods for analyzing large data sets, such as medical images, electronic health records, and genomics. Recent advances in Deep Learning (DL) provide an analysis framework that can be used to automatically classify images and objects with (and occasionally exceeding) human-level accuracy. A key advantage of Deep Learning is its ability to perform unsupervised feature extraction over massive data sets making big data part of the solution -- not the problem. Deep Learning is rapidly becoming a key tool at many of the top technology companies around the world. This talk will introduce DL in the broader context of machine learning and discuss critical factors driving the success of DL with examples of how deep learning is advancing healthcare. We will also outline development and deployment workflows for building powerful DL solutions and provide an overview of relevant open source tool kits, companies, and products. We will wrap up with a short demo of NVIDIA's DIGITS training system for rapidly prototyping your own deep learning applications.
Subjects:	Biomedical Research--trends Datasets as Topic Delivery of Health Care--trends Machine Learning Neural Networks, Computer
Publication Types:	Lecture Webcast

NLM Classification:	W 26.55.A7
NLM ID:	101693817
CIT Live ID:	19717
Permanent link:	https://videocast.nih.gov/watch=19717

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