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Keynote Lecture


Brain-inspired medical image analysis for computer-aided diagnosis

Bart M. ter Haar Romeny
Eindhoven University of Technology

Brief Bio

Bart M. ter Haar Romeny received the MSc degree in Applied Physics from Delft University of Technology in 1978, Ph.D. from Utrecht University in 1983 in biophysics. He became principal physicist of the Utrecht University Hospital Radiology Department. He was co-founder and associate professor at the Image Sciences Institute (ISI) of Utrecht University (1989-2001). From 2001, ter Haar Romeny holds the chair of Biomedical Image Analysis at the Department of Biomedical Engineering of Eindhoven University of Technology in the Netherlands, and since 2011 is appointed distinguished professor at Northeastern University, Shenyang, China. He closely collaborates with Philips Healthcare and Philips Research, other industries and (national and international) hospitals and research groups. Currently he is project leader of the Sino-Dutch RetinaCheck project, a large screening project for early detection of diabetic retinopathy in Liaoning, China.

He authored an interactive tutorial book on multi-scale computer vision techniques, edited a book on non-linear diffusion theory in computer vision and is involved in (resp. initiated) a number of international collaborations on these subjects. He is author/co-author of over 200 refereed journal and conference papers, 12 books and book chapters, and holds 2 patents. He supervised 29 PhD students, of which 4 graduated cum laude, and over 140 Master students. He is senior member of IEEE, associate member of the Chinese Brainnetome consortium, visiting professor at the Chinese Academy of Sciences in Beijing, member of the Governing Board of IAPR, Fellow of EAMBES, and chairman of the Dutch Society for Pattern Recognition and Image Processing.

Discoveries on brain mechanisms have really taken off. Modern optical and new MRI technologies give insight in this spectacular organ, especially in the field of vision. Of mutual benefit are new developments in deep learning and neural network modeling, the mathematical understanding, and the availability of massively parallel computing power. The lecture will address a number of lessons to learn from the brain for medical computer-aided diagnosis, explain the mathematical intuition of a number of algorithms, and show some remarkable successes booked so far.