Quantitative radiological imaging: an overview of current methods and challenges
Peter Savadjiev - Department of Diagnostic Radiology at McGill University
May 4, 2018, 2:30 p.m. - May 4, 2018, 3:30 p.m.
MCENG 103
Hosted by: Kaleem Siddiqi
Medical imaging has long been an important research focus in the computer sciences, however, there has always been an important 'adoption gap' between technology developed by the computer science community and the actual tools used by radiologists in clinical research and practice. In this talk, I will present some areas of mismatch between what is currently developed by the computer vision and AI communities and what is actually needed by radiologists. I will discuss areas that need to be developed further to achieve clinical impact. To that goal, I will present an overview of some computer vision research projects that I'm involved in at the McGill department of Radiology, and which could benefit from the involvement of SOCS graduate students.
Peter Savadjiev's research focuses in medical image analysis methodology with applications to neuroimaging, cardiac imaging, and abdominal imaging. He obtained a PhD degree in Computer Science from McGill University. He pursued a post-doctoral fellowship at Harvard Medical School from 2008 to 2010, following which he joined the faculty at Harvard Medical School, first as Instructor 2010-2015, and then as an Assistant Professor in Psychiatry and Radiology in 2015. Dr. Savadjiev joined the faculty in the Department of Diagnostic Radiology at McGill University in 2016 as Assistant Professor. He is also currently an Associate Member of the McGill School of Computer Science.
