Encrypted | Login

AAPM Virtual Library

• Login
• AAPM
• Public & Media
• International
• Medical Physicist
• Membership
• Students
• Meetings
• Education
• Quality & Safety
• Government Affairs
• Publications
• Career Services
• Corporate Affiliates
• Links of Interest
• Advertising Opportunities

2021 AAPM Virtual 63rd Annual Meeting - Session: SIIM-AAPM Joint Symposium on Machine Intelligence in Medical Imaging

Tweet #link

Joint Optimization of Learning-Based Image Reconstruction and K-Space Trajectories for MRI Jeffrey Fessler University of Michigan

When you have finished the video, you may watch again, browse/search all videos, go back to the previous page, or watch another video in this session (see below). add this video to a playlist.



All videos in this session:

	Introduction - Paul Nagy, PhD Johns Hopkins University
	Challenges and Opportunities in Validation of AI for Medical Imaging - Bennett Landman Vanderbilt University Medical Center
	Harnessing AI to Enable Advanced Image-Guidance in Interventional Procedures - Kristy Brock, PhD UT MD Anderson Cancer Center
	Standards-Based Integration of AI Tools Into Clinical Practice - Katherine Andriole, PhD Brigham & Women's Hospital, Harvard Medical School
	Q&A - Jeffrey Siewerdsen, PhD Johns Hopkins University

Graph includes 12 member loads and 370 non-member loads (382 total loads). Exclude non-member? A load is defined as someone coming to this page, not necesarily that they pressed play, or watched the entire presentation.

AAPM is the American Association of Physicists in Medicine.
1631 Prince Street
Alexandria, VA 22314
Phone 571-298-1300
Fax 571-298-1301 
Send general questions to 2023.aapm@aapm.org

AAPM's Privacy Policy
Use of the site constitutes your acceptance to its terms and conditions.

AAPM is a scientific, educational, and professional nonprofit organization devoted to the discipline of physics in medicine. The information provided in this website is offered for the benefit of its members and the general public, however, AAPM does not independently verify or substantiate the information provided on other websites that may be linked to this site.