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Medical Image Detection, Segmentation, and Parsing
Date: 2017/4/15             Browse: 97

Speaker:    Dr. S. Kevin Zhou
Time:         Apr 20, 10:30 am – 11:30am.
Location:   Aditorium, SIST Building
Invitor:      Prof. Jingyi Yu


Although medical image detection and segmentation has been actively studied over the past two decades, conventional methods mostly focus on detection and segmentation algorithms for a single object typically with some manual inputs. Recently with the availability of large datasets and the advance of statistical machine learning, a new trend that goes beyond the conventional wisdom has emerged, that is , fully automatic approaches for recognizing or detecting multiple objects from an image and further segmenting to parse a medical image into a cohort of anatomical structures have gained more prevalence in the literature. In this talk, I will introduce research challenges in designing such algorithms and present our machine/ddep learning based methods that leverage anatomical context embedded in the medical images for efficient and effective medical image parsing, along with demonstrations of real product contributions.

Dr. S. Kevin Zhou obtained his Ph.D. degree in Electrical Engineering from University of Maryland under the supervision of Professor Rama Chellappa and is currently a Principal Key Expert at Siemens Healthineers Technology Center, dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine learning and their applications to medical image analysis, face recognition and modeling, etc. Dr. Zhou has published 170+ book chapters and peer-reviewed journal and conference papers, has registered 250+ patents and inventions, has written two research monographs -- "Unconstrained face recognition" (jointly with R. Chellappa and W. Zhao) and "Recognition of humans and their activities using video" (jointly with R. Chellappa and A. Roy-Chowdhury), and has edited three books -- including "Medical image recognition, segmentation and parsing: machine learning and multiple object approaches" (single-editor) and "Deep learning for medical image analysis" (jointly with H. Greenspan and D. Shen). In addition, he has actively served the community, being associate editor for Medical Image Analysis and IEEE Trans. Medical Imaging, area chair and program committee member for premier computer vision and medical imaging conferences, giving tutorial talks, and organizing workshops. Dr. Zhou has won multiple awards that honor his publications, patents and products, including Best Paper Awards, Thomas Alva Edison Patent Award from NJ R&D Council, Johnson & Johnson Supplier Enable Innovation (SEI) Awards, R&D 100 Award or Oscar of Invention, Siemens Inventor of the Year, and University of Maryland ECE Distinguished Alumni Award. He is a fellow of American Institute of Medical and Biological Engineering (AIMBE).

 SIST-Seminar 17010