ACR2017LS05 - Deep Learning, Clinical Data Science and Radiology

May 23, 2017 10:00am ‐ May 23, 2017 12:00pm


CE Credits
2 – CME

The purpose of this session is to explain what are machine and deep learning, their role in radiology research and practice, and how to prepare for them. This session will emphasize how radiologists can approach machine learning and deep learning, separating hype from reality. It will demonstrate how these self-improving algorithms will help radiologists, and the challenges of producing valid and useful computer models. Finally, it will present a roadmap for machine and deep learning in radiology, and ACR's roles as this technology evolves.

Learning Objectives
Upon completion of this session, participants will be able to:

  • Describe the basics of machine learning and deep learning.
  • Discuss the roles these software tools will play in radiology, and how they can help radiologists.
  • Identify what is needed to bring valid and useful machine and deep learning algorithms into widespread clinical practice, and ACR's roles as this technology evolves.

What Should Radiologists Think About Machines That Think
10:00 AM - 10:25 AM Duration: 25 minutes
Raym Geis, MD, FACR

A Deep Dive into Deep Learning
10:25 AM - 10:50 AM Duration: 25 minutes
Garry Choy, MD

Radiology's Clinical Data Science Road Map
10:50 AM - 11:35 AM Duration: 45 minutes
Keith Dreyer, DO, PhD, FACR

Questions and Answers
11:35 AM - 12:00 PM Duration: 25 minutes
Raym Geis, MD, FACR
Garry Choy, MD
Keith Dreyer, DO, PhD, FACR


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