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GTC ON-DEMAND

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Abstract:
Learn about the key types of clinical use cases for AI methods in medical imaging beyond image classification that will ultimately improve medical practice. We'll explain the critical challenges and progress in applying AI in these applications, and describe the types of medical imaging and the clinical applications for deep learning to improve image interpretation. We will also talk about recent AI projects that tackle the challenging problem of clinical prediction with innovative approaches that provide explanations about AI model predictions to improve clinician acceptance.
Learn about the key types of clinical use cases for AI methods in medical imaging beyond image classification that will ultimately improve medical practice. We'll explain the critical challenges and progress in applying AI in these applications, and describe the types of medical imaging and the clinical applications for deep learning to improve image interpretation. We will also talk about recent AI projects that tackle the challenging problem of clinical prediction with innovative approaches that provide explanations about AI model predictions to improve clinician acceptance.  Back
 
Topics:
AI in Healthcare, AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9115
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Abstract:

Learn about the key types of clinical use cases for AI methods in medical imaging beyond simple image classification that will ultimately improve medical practice, as well as the critical challenges and progress in applying AI to these applications. We''ll first describe the types of medical imaging and the key clinical applications for deep learning for improving image interpretation. Next, we''ll describe recent developments of word-embedding methods to leverage narrative radiology reports associated with images to generate automatically rich labels for training deep learning models and a recent AI project that pushes beyond image classification and tackles the challenging problem of clinical prediction. We''ll also describe emerging methods to leverage multi-institutional data for creating AI models that do not require data sharing and recent innovative approaches of providing explanation about AI model predictions to improve clinician acceptance.

Learn about the key types of clinical use cases for AI methods in medical imaging beyond simple image classification that will ultimately improve medical practice, as well as the critical challenges and progress in applying AI to these applications. We''ll first describe the types of medical imaging and the key clinical applications for deep learning for improving image interpretation. Next, we''ll describe recent developments of word-embedding methods to leverage narrative radiology reports associated with images to generate automatically rich labels for training deep learning models and a recent AI project that pushes beyond image classification and tackles the challenging problem of clinical prediction. We''ll also describe emerging methods to leverage multi-institutional data for creating AI models that do not require data sharing and recent innovative approaches of providing explanation about AI model predictions to improve clinician acceptance.

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Topics:
AI in Healthcare, Medical Imaging & Radiology
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8295
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Abstract:
Learn about some of the key opportunities for deep learning in medical imaging, some of the current challenges, and exciting recent developments that are tackling them. We'll begin with a brief overview of medical imaging, current challenges for human observers of these images, and key applications for deep learning for improving image interpretation. We'll follow with descriptions of several specific use cases for deep learning in radiology, pathology, urology, and ophthalmology imaging, including improvements in image diagnosis that are besting state-of-the-art computerized diagnosis algorithms, approaches for visualizing and explaining to physicians what deep networks have learned to improve confidence in using the information they provide to guide decision making, and new, freely available tools to dramatically enhance the efficiency of creating new deep learning models. We'll provide links for more information about tools and information so attendees can try their hand at tackling problems in this exciting domain. Finally, we'll give a live demonstration for a portable deep learning package optimized for medical imaging.
Learn about some of the key opportunities for deep learning in medical imaging, some of the current challenges, and exciting recent developments that are tackling them. We'll begin with a brief overview of medical imaging, current challenges for human observers of these images, and key applications for deep learning for improving image interpretation. We'll follow with descriptions of several specific use cases for deep learning in radiology, pathology, urology, and ophthalmology imaging, including improvements in image diagnosis that are besting state-of-the-art computerized diagnosis algorithms, approaches for visualizing and explaining to physicians what deep networks have learned to improve confidence in using the information they provide to guide decision making, and new, freely available tools to dramatically enhance the efficiency of creating new deep learning models. We'll provide links for more information about tools and information so attendees can try their hand at tackling problems in this exciting domain. Finally, we'll give a live demonstration for a portable deep learning package optimized for medical imaging.  Back
 
Topics:
Artificial Intelligence and Deep Learning, AI in Healthcare, Healthcare and Life Sciences, Computer Vision, Medical Imaging & Radiology
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7639
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