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Session: Assessment of Deep-Learning Technologies in Medical Imaging: From Imaging Science to Clinical Practice [Return to Session]

Assessment of Deep-Learning Technologies in Medical Imaging: From Imaging Science to Clinical Practice

V Kelkar1*, L Yu2*, F Liu3*, (1) University of Illinois at Urbana-Champaign, Urbana-champaign, IL, (2) Mayo Clinic, Rochester, MN, (3) Massachusetts General Hospital, Boston, MA

Presentations

TH-C-207-0 (Thursday, 7/14/2022) 10:00 AM - 11:00 AM [Eastern Time (GMT-4)]

Room 207

Learning Objectives:

1. Introduce state-of-the-art DL-based imaging techniques and their applications in medical imaging.
2. Understand the opportunities and risks associated with the application of DL-based methods to medical imaging.
3. Understand the basic principles of task-based image quality assessment and its applications in optimizing medical imaging systems.
4. Understand the application of generative models such as generative adversarial networks (GANs) to imaging science.

Keywords

Not Applicable / None Entered.

Taxonomy

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