报告题目:The impact of deep learning and artificial intelligence on radiology
报告人：Ronald M. Summers, MD, PhD, FSAR
Senior Investigator and Staff Radiologist
Chief, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory
Radiology and Imaging Sciences
National Institutes of Health Clinical Center, USA
报告人简介：Ronald M. Summers received the B.A. degree in physics and the M.D. and Ph.D. degrees in Medicine/Anatomy & Cell Biology from the University of Pennsylvania.He completed a medical internship at the Presbyterian-University of Pennsylvania Hospital, Philadelphia, PA, a radiology residency at the University of Michigan, Ann Arbor, MI, and an MRI fellowship at Duke University, Durham, NC.In 1994, he joined the Radiology and Imaging Sciences Department at the NIH Clinical Center in Bethesda, MD where he is now a tenured Senior Investigator and Staff Radiologist.In 2013, he was named a Fellow of the Society of Abdominal Radiologists. He directs the Imaging Biomarkers and Computer-Aided Diagnosis (CAD) Laboratory and is former and founding Chief of the NIH Clinical Image Processing Service.In 2000, he received the Presidential Early Career Award for Scientists and Engineers, presented by Dr. Neal Lane, President Clinton’s science advisor.In 2012, he received the NIH Director’s Award, presented by NIH Director Dr. Francis Collins. In 2017, he received the NIH Clinical Center Director’s Award. His research interests include deep learning, virtual colonoscopy, CAD and development of large radiologic image databases.His clinical areas of specialty are thoracic and abdominal radiology and body cross-sectional imaging.He is a member of the editorial boards of the Journal of Medical Imaging, Radiology:Artificial Intelligence and Academic Radiology and a past member of the editorial board of Radiology. He is a program committee member of the Computer-aided Diagnosis section of the annual SPIE Medical Imaging conference and is co-chair of the entire conference in 2018 and 2019. He was Program Co-Chair of the 2018 IEEE ISBI symposium. He has co-authored over 400 journal, review and conference proceedings articles and is a co-inventor on 14 patents.
报告内容简介：Building upon major advances in computer science, there has been an explosion of research interest in the use of deep learning in radiology. In this presentation, I will show how deep learning has led to major performance improvements in radiology image analysis, including automated body part recognition, lesion segmentation and detection. I will discuss how deep learning may enable fully-automated radiology image interpretation. Finally, I will show how deep learning systems can be trained using large numbers (>100,000) of radiology images and text reports.