• Keynote Speakers 主讲嘉宾


    Prof. Wan-Chi Siu
    IEEE Life Fellow & IET Fellow

    Hong Kong Polytechnic University, Hong Kong, China

    Wan-Chi Siu received the MPhil and PhD degrees from The Chinese University of Hong Kong in 1977 and Imperial College London in 1984. He is Life-Fellow of IEEE and Fellow of IET. Prof. Siu is now Emeritus Professor, was Chair Professor, Founding Director of Signal Processing Research Centre, Head of Electronic and Information Engineering Department and Dean of Engineering Faculty of The Hong Kong Polytechnic University, and is also Research Professor of Caritas Institute of Higher Education. He is an expert in DSP, transforms, fast algorithms, machine learning, deep learning, super-resolution imaging, 2D and 3D video coding, object recognition and tracking. He has published 500 research papers (over 200 appeared in international journals), and edited three books. He has also 9 recent patents granted. Prof. Siu was an independent non-executive director (2000-2015) of a publicly-listed video surveillance company and convenor of the First Engineering/IT Panel of the RAE(1992/93) in Hong Kong. He is an outstanding scholar, with many awards, including the Best Teacher Award, the Best Faculty Researcher Award (twice) and IEEE Third Millennium Medal (2000). Prof. Siu has been Guest Editor/Subject Editor/AE for IEEE Transactions on Circuits and System II, Image Processing, Circuit & System for Video Technology, and Electronics Letters, and organized very successfully over 20 international conferences including IEEE society-sponsored flagship conferences: such as TPC Chair of ISCAS1997 and General Chair of ICASSP2003 and General Chair of ICIP2010. He was Past-President (2017-2018) of APSIPA (Asia-Pacific Signal and Information Processing Association), and Vice-President, Chair of Conference Board and Core Member of Board of Governors (2012-2014) of the IEEE Signal Processing Society, and has been a member of the IEEE Educational Activities Board, IEEE Fourier Award for Signal Processing Committee (2017-2020), Hong Kong RGC Engineering Panel Member-JRS (2020-2022), and some other IEEE Technical Committees.

    Prof. S. Kevin Zhou
    IEEE Fellow

    Suzhou Institute for Advanced Research, University of Science and Technology of China, China

    Prof. S. Kevin Zhou obtained his PhD degree from University of Maryland, College Park. Currently he is a professor and executive dean of School of Biomedical Engineering, Suzhou Institute for Advanced Research, University of Science and Technology of China (USTC) and an adjunct professor at Institute of Computing Technology, Chinese Academy of Sciences and Chinese University of Hong Kong (CUHK), Shenzhen. Prior to this, he was a principal expert and a senior R&D director at Siemens Healthcare Research. Dr. Zhou has published 240+ book chapters and peer-reviewed journal and conference papers, registered 140+ granted patents, written two research monographs, and edited three books. The two recent books he led the edition are entitled "Deep Learning for Medical Image Analysis, SK Zhou, H Greenspan, DG Shen (Eds.)" and "Handbook of Medical Image Computing and Computer Assisted Intervention, SK Zhou, D Rueckert, G Fichtinger (Eds.)". He has won multiple awards including R&D 100 Award (Oscar of Invention), Siemens Inventor of the Year, and UMD ECE Distinguished Alumni Award. He has been a program co-chair for MICCAI2020, an editorial board member for IEEE Trans. Medical Imaging and Medical Image Analysis, and an area chair for AAAI, CVPR, ICCV, MICCAI, and NeurIPS. He has been elected as a treasurer and board member of the MICCAI Society, an advisory board member of MONAI (Medical Open Network for AI), and a fellow of AIMBE, IEEE, and NAI (National Academy of Inventors).

    Speech Title: Traits and Trends of AI in Medical Imaging

    Artificial intelligence or deep learning technologies have gained prevalence in solving medical imaging tasks. In this talk, we first review the traits that characterize medical images, such as multi-modalities, heterogeneous and isolated data, sparse and noisy labels, imbalanced samples. We then point out the necessity of a paradigm shift from "small task, big data" to "big task, small data". Finally, we illustrate the trends of AI technologies in medical imaging and present a multitude of algorithms that attempt to address various aspects of “big task, small data”:
    Annotation-efficient methods that tackle medical image analysis without many labelled instances, including one-shot or label-free inference approaches.
    “Deep learning + knowledge modeling" approaches, which integrate machine learning with domain knowledge to enable start-of-the-art performances for many tasks of medical image reconstruction, recognition, segmentation, and parsing.
    Universal models that learn “identical + differential” feature representations for multi-domain tasks to unleash the potential of ‘bigger data’, which are formed by integrating multiple datasets associated with heterogeneous tasks into one use.

    Prof. Ce Zhu
    IEEE Fellow

    University of Electronic Science and Technology of China, China

    Ce Zhu is currently a Professor with the School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China. His research interests include image/video coding and communications, 3D video, visual analysis and understanding, visual perception and applications. He has served on the editorial boards of a few journals, including as an Associate Editor of IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Broadcasting, IEEE Signal Processing Letters, and IEEE Communications Surveys and Tutorials. He has also served as a Guest Editor of a few special issues in international journals, including as a Guest Editor in the IEEE Journal of Selected Topics in Signal Processing. He is a Fellow of the IEEE, and an IEEE CASS Distinguished Lecturer (2019-2020).