SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC On-Demand

Presentation
Media
Abstract:

This customer panel brings together AI implementers who have deployed deep learning at scale. The discussion will focus on specific technical challenges they faced, solution design considerations, and best practices learned from implementing their respective solutions.

This customer panel brings together AI implementers who have deployed deep learning at scale. The discussion will focus on specific technical challenges they faced, solution design considerations, and best practices learned from implementing their respective solutions.

  Back
 
Topics:
AI and DL Business Track (high level), Data Center and Cloud Infrastructure, Deep Learning and AI Frameworks
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9121
Streaming:
Share:
 
Abstract:
We'll talk about overcoming the barrier to obtaining adequate data to generate highly accurate models in medical imaging. Deep learning has proven valuable in creating analytic tools for medical imaging that have been cleared in multiple countries and are used in daily clinical work. But one barrier to additional solutions has been a lack of available data. Data are often difficult to obtain, especially in the quantities needed to generate highly accurate models. We'll discuss data augmentation techniques and explain how three different institutions have overcome this barrier.
We'll talk about overcoming the barrier to obtaining adequate data to generate highly accurate models in medical imaging. Deep learning has proven valuable in creating analytic tools for medical imaging that have been cleared in multiple countries and are used in daily clinical work. But one barrier to additional solutions has been a lack of available data. Data are often difficult to obtain, especially in the quantities needed to generate highly accurate models. We'll discuss data augmentation techniques and explain how three different institutions have overcome this barrier.  Back
 
Topics:
AI in Healthcare, Advanced AI Learning Techniques (incl. GANs and NTMs), Medical Imaging and Radiology
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9995
Streaming:
Download:
Share:
 
Abstract:

We will introduce deep learning applications in clinical neuroimaging (using MRI, CT, PET, etc.) and recent breakthrough results from Stanford and Subtle Medical. Perspectives and feedbacks of applying AI technologies in neuroimaging are shared, from expert radiologists and deep learning experts. How Deep Learning/AI is changing clinical neuroimaging practice * How will deep learning be applied in radiology workflow right now and in the future * Practical concerns and perspectives from radiologists How Deep Learning assists smarter neuroimaging decision making * Multi-scale 3D network enables lesion outcome prediction for stroke * More accurate lesion segmentation in neuroimaging How Deep Learning enables safer and cheaper neuroimaging screening * Deep Learning and GAN enables >95% reduction in radiation for functional medical imaging * Deep Learning enables 90% reduction in chemical (Gadolinium) contrast agent usage in contrast enhanced MRI How Deep Learning accelerate neuroimaging * Further acceleration and improved MRI reconstruction using deep learning * Deep Generative Adversarial Network for Compressed Sensing

We will introduce deep learning applications in clinical neuroimaging (using MRI, CT, PET, etc.) and recent breakthrough results from Stanford and Subtle Medical. Perspectives and feedbacks of applying AI technologies in neuroimaging are shared, from expert radiologists and deep learning experts. How Deep Learning/AI is changing clinical neuroimaging practice * How will deep learning be applied in radiology workflow right now and in the future * Practical concerns and perspectives from radiologists How Deep Learning assists smarter neuroimaging decision making * Multi-scale 3D network enables lesion outcome prediction for stroke * More accurate lesion segmentation in neuroimaging How Deep Learning enables safer and cheaper neuroimaging screening * Deep Learning and GAN enables >95% reduction in radiation for functional medical imaging * Deep Learning enables 90% reduction in chemical (Gadolinium) contrast agent usage in contrast enhanced MRI How Deep Learning accelerate neuroimaging * Further acceleration and improved MRI reconstruction using deep learning * Deep Generative Adversarial Network for Compressed Sensing

  Back
 
Topics:
AI in Healthcare, Medical Imaging and Radiology
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8647
Streaming:
Share:
 
Abstract:
Advanced computation powered by GPUs is changing the clinical decision-making process. We'll present an exciting example of using NVIDIA GPUs for multi-contrast magnetic resonance imaging exams. Neurological disorders result in great clinical challenges and high societal burdens. Multi-contrast MRI exams are frequently used for diagnosis because the various tissue contrasts provides complementary diagnosis information to distinguish normal tissue from pathology. However, the cost of acquiring these multiple sequences is extensive scanning time, which significantly increases both the diagnosis cost and patients' discomfort and limit the acquired image quality. We'll propose a new approach to accelerate multi-contrast imaging using a deep learning approach powered by GPUs. Validated on both patients and healthy subjects, we'll demonstrate that we can significantly reduce scanning time while improving image resolution and quality and preserving the diagnostic information.
Advanced computation powered by GPUs is changing the clinical decision-making process. We'll present an exciting example of using NVIDIA GPUs for multi-contrast magnetic resonance imaging exams. Neurological disorders result in great clinical challenges and high societal burdens. Multi-contrast MRI exams are frequently used for diagnosis because the various tissue contrasts provides complementary diagnosis information to distinguish normal tissue from pathology. However, the cost of acquiring these multiple sequences is extensive scanning time, which significantly increases both the diagnosis cost and patients' discomfort and limit the acquired image quality. We'll propose a new approach to accelerate multi-contrast imaging using a deep learning approach powered by GPUs. Validated on both patients and healthy subjects, we'll demonstrate that we can significantly reduce scanning time while improving image resolution and quality and preserving the diagnostic information.  Back
 
Topics:
Healthcare and Life Sciences, AI in Healthcare, Deep Learning and AI, Medical Imaging and Radiology, Video and Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7415
Download:
Share: