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GTC ON-DEMAND

Presentation
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Abstract:
Major advances in computer science are beginning to have an impact on radiology. The rapid achievements in performance for object detection in natural images have enabled these impacts. There has been an explosion of research interest and number of publications regarding the use of deep learning in radiology. We'll show examples of how deep learning has led to major performance improvements in radiology image analysis, including image segmentation and computer-aided diagnosis.
Major advances in computer science are beginning to have an impact on radiology. The rapid achievements in performance for object detection in natural images have enabled these impacts. There has been an explosion of research interest and number of publications regarding the use of deep learning in radiology. We'll show examples of how deep learning has led to major performance improvements in radiology image analysis, including image segmentation and computer-aided diagnosis.  Back
 
Topics:
Medical Imaging & Radiology, AI in Healthcare
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7130
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Abstract:
"AI is transforming healthcare" is the buzz around every news alert these daysbut is it true? Where is AI and deep learning affecting healthcare and how is it impacting the medical imaging space? Join a thought leadership panel as government, industry and academic experts discuss the real calibration of the spaceseparating reality from the noise to explore how deep learning is advancing clinical practice, including advancements to overcome data and regulatory challenges.
"AI is transforming healthcare" is the buzz around every news alert these daysbut is it true? Where is AI and deep learning affecting healthcare and how is it impacting the medical imaging space? Join a thought leadership panel as government, industry and academic experts discuss the real calibration of the spaceseparating reality from the noise to explore how deep learning is advancing clinical practice, including advancements to overcome data and regulatory challenges.  Back
 
Topics:
Leadership and Policy in AI, AI in Healthcare
Type:
Panel
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7246
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Abstract:

There are 14 million new cancer cases and 8.2 million cancer-related deaths worldwide per year.Innovation in the fight against cancer requires a multi-faceted approach. As patients and as stakeholders, healthcare ecosystem experts in genomics, proteomics, imaging, medicine and data sciences are cooperating in new ways. GPU computing, integrated data and novel algorithms enable the use of deep learning and artificial intelligence to transform cancer research and care. Dr. Jerry S.H. Lee, Whitehouse Cancer Moonshot Deputy Director for Research and Technology, will facilitate a thought provoking panel discussion on leveraging Artificial Intelligence to fight cancer.

There are 14 million new cancer cases and 8.2 million cancer-related deaths worldwide per year.Innovation in the fight against cancer requires a multi-faceted approach. As patients and as stakeholders, healthcare ecosystem experts in genomics, proteomics, imaging, medicine and data sciences are cooperating in new ways. GPU computing, integrated data and novel algorithms enable the use of deep learning and artificial intelligence to transform cancer research and care. Dr. Jerry S.H. Lee, Whitehouse Cancer Moonshot Deputy Director for Research and Technology, will facilitate a thought provoking panel discussion on leveraging Artificial Intelligence to fight cancer.

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Topics:
Healthcare and Life Sciences
Type:
Panel
Event:
GTC Washington D.C.
Year:
2016
Session ID:
DCS16179
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Abstract:

Major advances in computer science are beginning to have an impact on radiology. The rapid achievements in performance for object detection in natural images have enabled these impacts. There has been an explosion of research interest and number of publications regarding the use of deep learning in radiology. In this presentation, we'll show examples of how deep learning has led to major performance improvements in radiology image analysis, including image segmentation and computer aided diagnosis.

Major advances in computer science are beginning to have an impact on radiology. The rapid achievements in performance for object detection in natural images have enabled these impacts. There has been an explosion of research interest and number of publications regarding the use of deep learning in radiology. In this presentation, we'll show examples of how deep learning has led to major performance improvements in radiology image analysis, including image segmentation and computer aided diagnosis.

  Back
 
Topics:
Healthcare and Life Sciences, HPC and AI
Type:
Talk
Event:
GTC Washington D.C.
Year:
2016
Session ID:
DCS16111
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