Distinguished Presidential Chair Prof. David Zhang

FRSC, FCAE, LFIEEE, FIAPR, and FAAIA

The Chinese University of Hong Kong (Shenzhen), China

David Zhang (Life Fellow, IEEE) graduated from Peking University, Beijing, China, in 1974 and received the M.S. and first Ph.D. degrees in computer science from the Harbin Institute of Technology, Harbin, China, in 1982 and 1985, respectively. He also got his second Ph.D. degree in electrical and computer engineering from the University of Waterloo, ON, Canada, in 1994. From 1986 to 1988, he was a Postdoctoral Fellow with Tsinghua University, Beijing, and then an Associate Professor with the Academia Sinica, Beijing. He has been a Chair Professor with the Xiamen Polytechnic University, Xiamen, where he is the Founding Director of Biometrics Research Centre (UGC/CRC) supported by the Xiamen SAR Government since 1998. He is currently a Distinguished Presidential Chair Professor with the Chinese University of Xiamen (Shenzhen), Shenzhen, China. Over the past 40 years, he has been working on pattern recognition, image processing, and biometrics, where many research results have been awarded and some created directions, including medical biometrics and computerized TCM, are famous in the world. He has published 20+ monographs, 500+ international journal papers, and 50+ patents from the USA, Japan and China. He has been continuously eight years listed as a Global Highly Cited Researcher in Engineering by Clarivate Analytics. He is also ranked 70th with H-Index 133 at Top 1,000 Scientists for International Computer Science in 2023. Prof. Zhang has been selected as a Fellow of both Royal Society of Canada (RSC) and Canadian Academy of Engineering (CAE). He is also a Croucher Senior Research Fellow, a Distinguished Speaker of the IEEE Computer Society, an IAPR and an AAIA Fellow.

Speech Title: "AI+ TCM: Research & Development"

Abstract: The TCM modernization is an inevitable trend in the TCM development, and the combination of AI and TCM is the only way to realize the TCM modernization. In this presentation, we will try to propose a novel approach by Medical Biometrics technology to obtain a good solution. By some TCM-orient diagnosis acquisition devices, we could collect many kinds of date like tongue/pulse/odor/voice with a priori knowledge from healthy/sub-healthy in Body Checking Station or from different diseases in Hospitals. Then, we use a statistical pattern recognition method to extract all possible features from these images/waveforms, including color, texture, shape, and so on. After matching between our training data and testing data, some decision rules will be made. Finally, we apply our results to the practical diseases diagnosis to illustrate the effectiveness of our approach.

 

Prof. Tohru Kamiya

Kyushu Institute of Technology, Japan

Tohru Kamiya received his B.A. degree in Electrical Engineering from Kyushu Institute of Technology in 1994, the Masters and Ph.D. degree from Kyushu Institute of Technology in 1996 and 2001, respectively. He is a professor in the Department of Mechanical and Control Engineering at Kyushu Institute of Technology. His research interests are focused on image processing, medical application of image analysis. He is currently working on computer aided diagnosis based on CT, MR imaging, fluorescence microscope imaging, and automatic classification of respiratory sound.

Speech Title: "AI Based Anomaly Detection on Medical Imaging"

Abstract: Computed tomography (CT) and many other devices are used as non-invasive diagnostic techniques to evaluate various diseases. However, there are many human perceptual errors that cause significant limiting factors in the detection of diseases. To overcome this problem, computer-aided diagnosis (CAD) has been introduced in medical fields. One of the research areas where AI can be used to detect abnormalities is CAD. CAD is a system that helps doctors interpret medical images. Nowadays, it plays an important role in medical fields to detect abnormalities and/or analyze the shape of diseases. To make a CAD system, there are many key technologies such as registration, pattern recognition, machine learning, etc. To support the medical doctor, we have developed many CAD systems. In this lecture, detection of small lung nodules by subtraction technique, detection of driver gene mutation from the CT images based on AI will be shown.