Speaker: Prof. Eliot L. Siegel, MD
Professor and Vice Chairman Dept. Diagnostic Radiology and Nuclear Medicine
Chief Imaging
VA Maryland Healthcare System
University of Maryland School of Medicine
Baltimore, Maryland, USA

Eliot Siegel is Professor and Vice Chair at the University of Maryland School of Medicine, Department of Diagnostic Radiology, as well as Chief of Radiology and Nuclear Medicine for the Veterans Affairs Maryland Healthcare System. He is the director of the Maryland Imaging Research Technologies Laboratory and has adjunct appointments as Professor of Bioengineering at the University of Maryland College Park and as Professor of Computer Science at the University of Maryland Baltimore County. Dr. Siegel was responsible for the NCI's National Cancer Image Archive and served as Workspace Lead of the National Cancer Institute's caBIG In Vivo Imaging Workspace. He has been named as Radiology Researcher and Radiology Educator of the year by his peers as well as one of the Top Ten radiologists. Under his leadership, the VA Maryland Healthcare System became the first filmless healthcare enterprise in the world. He has written over 200 articles and book chapters about PACS (Picture Archiving and Communication Systems) and digital imaging, and has edited six books on the topic, including Filmless Radiology and Security Issues in the Digital Medical Enterprise. He has made more than 1,000 presentations throughout the world on a broad range of topics involving computer applications in imaging and medicine. Dr. Siegel served as symposium chairman for the Society of Photo-optical and Industrial Engineers (SPIE) Medical Imaging Meeting for three years and is currently serving on the board of directors of the Society of Computer Applications in Radiology. He is a fellow of the American College of Radiology and of the Society of Imaging Informatics in Medicine.

Presentation Title: Breaking New Ground with AI
The Promise: the transition from film to digital imaging almost 30 years ago brought about not only ubiquitous access to images, but also the tantalizing promise that “Artificial Intelligence” could be utilized for these digital images to achieve earlier and more accurate detection, diagnosis and treatment. Dr. Siegel will provide an overview of the incredible potential of AI using Convolutional Neural Networks (Deep Learning) to have a major positive impact on efficiency, accuracy, discoverability, safety and efficacy in diagnostic imaging, which will revolutionize the practice of diagnostic radiology over the next decade. This will allow our specialty to stay relevant and indeed critical as we enter the dawn of the era of personalized/precision medicine.

Speaker: Prof. Bram van Ginneken, PhD
Professor of Medical Image Analysis
Radboud University Medical Center
Nijmegen, the Netherlands

Bram van Ginneken is Professor of Medical Image Analysis at Radboud University Medical Center and chairs the Diagnostic Image Analysis Group. He also works for Fraunhofer MEVIS in Bremen, Germany, and is a founder of Thirona, a company that develops software and provides services for medical image analysis. He studied Physics at Eindhoven University of Technology and Utrecht University. In 2001, he obtained his PhD at the Image Sciences Institute on Computer-Aided Diagnosis in Chest Radiography. He has (co-)authored over 250 publications in international journals and is a member of the Editorial Board of Medical Image Analysis. He pioneered the concept of challenges in medical image analysis and developed grand-challenge.org.

Presentation Title: AI in Radiology: From Promise to Invisibility?
Deep learning, an obscure part of machine learning only a decade ago, has become the dominant technology in Artificial Intelligence. This has led to 181 AI products for radiology that are now commercially available in Europe. What was once a promise, is now a reality. Prof. van Ginneken will provide an overview of these products, present their scientific evidence and talk about the future of AI. We’ll discuss bottlenecks for widespread implementation of AI. And he will argue that next to all these products, AI is integrated, behind the scenes, in more and more software used by radiologists for reconstructing, viewing, and reporting imaging studies. Most AI will therefore become invisible.

Moderator: Richard T. Mather, PhD
President at Canon Medical Research USA
Vernon Hills, IL USA

Richard Mather is President of Canon Medical Research USA (CMRU) and leads the company’s strategic direction, innovating core medical imaging technology in imaging system physics, radiation detection, and image reconstruction for Canon Medical’s CT, MR, PET, and VL systems. Richard graduated from UCLA’s graduate program in Biomedical Physics in 1997 and has worked in advanced medical imaging for his entire career at companies such as Imatron, General Electric, Toshiba, and Canon. Richard has written more than 60 peer reviewed manuscripts and book chapters. He currently serves on the board of the Academy of Radiology and Bioimaging Research.