IEEE Journal of Translational Engineering in Health and Medicine's unique aim is to publish original work in the intersection of engineering and clinical translation. The journal's focus is interdisciplinary collaborations between researchers, healthcare providers, and industry and aims to publish results and best practices from these translational efforts.
FENG ZHAO received the B.Eng. degree in electronic engineering from the University of Science and Technology of China, Hefei, China, in 2000, and the M.Phil. and Ph.D. degrees in computer vision from The Chinese University of Hong Kong, Hong Kong, in 2002 and 2006, respectively. From 2006 to 2007, he was a Post-Doctoral Fellow with the Department of Information Engineering, The Chinese University of Hong Kong. From 2007 to 2010, he was a Research Fellow with the School of Computer Engineering, Nanyang Technological University, Singapore. He was a Post-Doctoral Research Associate with the Intelligent Systems Research Centre, University of Ulster, U.K. Since 2011, he has been with the Department of Computer Science, Swansea University, Swansea, U.K., where he is currently a Workshop Developer and Post-Doctoral Fellow. His research interests include image processing, biomedical image analysis, computer vision, pattern recognition, and machine learning.
JTEHM, Articles, Published ArticlesComputer Vision Techniques for Transcatheter Intervention
This paper provides a comprehensive review for researchers of computer vision techniques and methodologies in transcatheter intervention. Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and the treatment of cardiovascular diseases. For example, transcatheter aortic valve implantation is... Read more
Posted on 10 JUL 2015
TBME, Featured ArticlesConstructing Multi-view High-order Functional Connectivity Networks for Diagnosis of Autism Spectrum Disorder
To fully explore the discriminative information provided by different brain networks, a cluster-based multi-view high-order FCN (Ho-FCN) framework is proposed in this paper. Specifically, we first group the functional connectivity (FC) time series into different clusters and compute the multi-order central moment series for the FC time series in each cluster. Then we utilize the correlation of central moment series between different clusters to reveal the high-order FC relationships among multiple ROIs... Read more
Posted on 28 FEB 2022