Prof. Syoji Kobashi
University of Hyogo, Japan
Syoji Kobashi received BE in 1995, ME in 1997, and Doctor of Engineering in 2000, all from Himeji institute of Technology. He was an assistant professor at Himeji Institute of Technolog (2000-2004), an associate professor (2005-2016), currently a professor (2016-) and the manager of advanced medical engineering research center (2016-), University of Hyogo. And, he was a guest associate professor at Osaka University, WPI immunology frontier research center (2010-2016), and was a visiting scholar at University of Pennsylvania (2011-2012). His research interests include medical image understanding and artificial intelligence. He received 16 international awards, including Lifetime Achievement Award (WAC, 2016), Franklin V. Taylor Memorial Award (IEEE-SMCS, 2009). He has been serving on Program Chair of WAC2020, and many others. Moreover, he is an editor-at-large of Intelligent Automation & Soft Computing journal, an editor-in-chief of International Journal of Biomedical Soft Computing and Human Sciences, etc. He is the senior member of IEEE.
Speech Title: "Tips to Develop AI-Based-Computer-Aided Diagnosis Systems"
Abstract: Computer-aided diagnosis (CAD) in medical image understanding (MIU) is one of the expected fields that artificial intelligence (AI), especially deep learning (DL), improves the performance. However, DL alone is not enough to analyze medical images. DL process is just one processing step in the overall CAD system. Rather, the main role of researcher is to develop methods to synthesize data that are processed by the DL models, and methods that derive satisfactory results from the inferred results of the DL model. In order to discuss how to develop AI-based-CAD systems, I will introduce some CAD applications using DL models. It will include fatigue fracture detection in 3-D computer tomography (CT) images, tooth recognition in dental panorama radiograph, finger joint detection in hand radiograph. Through the applications, I am going to summarize the strategy to develop AI-based-CAD systems.
Prof. Xia Wu
Beijing Normal University, China
Dr. Xia Wu is presently the professor and doctoral supervisor of the School of Artificial Intelligence and the State Key Laboratory of Cognitive Neuroscience and Learning of Beijing Normal University. She has been engaged in intelligent algorithms exploration based on brain imaging data, neural feedback, brain disease diagnosis and prediction. Dr. Wu has published nearly 100 research papers on top journals and conferences, such as IEEE TNNLS, NeuroImage, Human Brain Mapping, Medical Image Analysis, IPMI, MICCAI and etc. She was supported by the Excellent Youth Foundation of NSFC, and won the first prize of Wu Wen Jun AI Science & Technology Award and the second prize of Natural Science Award of the Ministry of Education as the first accomplisher.
Speech Title: "The Algorithms of Spatio-Temporal Co-Variant Brain Network Analysis based on Neural Architecture Search"
Abstract: Brain functional network is a kind of topological structure that describes interaction and coordination of various functional brain areas, during human brain’s resting state or task state, which is one of important theoretical bases for deciphering the brain mechanism. How to construct the cognitive task-specific algorithm model structure, in order to analyze the spatio-temporal co-variant brain functional network accurately, is an important topic in the field of artificial intelligence and brain neuroscience. As an advanced brain-inspired framework, neural architecture search algorithms can search and optimize the deep model structure for specific cognitive tasks. In this topic, a series of neural architecture search algorithms for spatio-temporal co-variant brain functional networks will be presented based on brain data characteristics, in order to promote the study of brain cognitive functions and the intelligent algorithms for brain functional networks.
KEYNOTE SPEAKER OF IMIP 2021/主讲嘉宾
Prof. David Zhang, RSC Fellow, IEEE Fellow and IAPR Fellow
Chinese University of Hong Kong (Shenzhen), China
David Zhang graduated in Computer Science from Peking University. He received his MSc in 1982 and his PhD in 1985 in both Computer Science from the Harbin Institute of Technology (HIT), respectively. From 1986 to 1988 he was a Postdoctoral Fellow at Tsinghua University and then an Associate Professor at the Academia Sinica, Beijing. In 1994 he received his second PhD in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada. He has been a Chair Professor at the Hong Kong Polytechnic University where he is the Founding Director of Biometrics Research Centre (UGC/CRC) supported by the Hong Kong SAR Government since 2005. Currently he is Presidential Chair Professor in Chinese University of Hong Kong (Shenzhen). Over past 30 years, he have been working on pattern recognition, image processing and biometrics, where many research results have been awarded and some created directions, including medical biometrics and palmprint recognition, are famous in the world. So far, he has published over 20 monographs, 500 international journal papers and 40 patents from USA/Japan/HK/China. He has been continuously listed as a Highly Cited Researchers in Engineering by Clarivate Analytics during 2014-2020. He is also ranked about 80 with H-Index 120 at Top 1,000 Scientists for international Computer Science and Electronics. Recently Professor Zhang has been selected as a Fellow of the Royal Society of Canada. He also is a Croucher Senior Research Fellow, Distinguished Speaker of the IEEE Computer Society, and an IEEE Life Fellow and an IAPR Fellow.
Speech Title: "Advanced Biometrics"
Abstract: In recent times, an increasing, worldwide effort has been devoted to the development of automatic personal identification systems that can be effective in a wide variety of security contexts. As one of the most powerful and reliable means of personal authentication, biometrics has been an area of particular interest. It has led to the extensive study of biometric technologies and the development of numerous algorithms, applications, and systems, which could be defined as Advanced Biometrics. This presentation will systematically explain this new research trend. As case studies, a new biometrics technology (palmprint recognition) and two new biometrics applications (medical biometrics and aesthetical biometrics) are introduced. Some useful achievements could be given to illustrate their effectiveness.
Prof. Yen-Wei Chen
Ritsumeikan University, Japan
Yen-Wei Chen received the B.E. degree in 1985 from Kobe Univ., Kobe, Japan, the M.E. degree in 1987, and the D.E. degree in 1990, both from Osaka Univ., Osaka, Japan. He was a research fellow with the Institute for Laser Technology, Osaka, from 1991 to 1994. From Oct. 1994 to Mar. 2004, he was an associate Professor and a professor with the Department of Electrical and Electronic Engineering, Univ. of the Ryukyus, Okinawa, Japan. He is currently a professor with the college of Information Science and Engineering, Ritsumeikan University, Japan. He is the founder and the first director of Center of Advanced ICT for Medicine and Healthcare, Ritsumeikan University. He is also an adjunct professor with the College of Computer Science, Zhejiang University, and Zhejiang Lab, China. He was a visiting professor with the Oxford University, Oxford, UK in 2003 and a visiting professor with Pennsylvania State University, USA in 2010. His research interests include medical image analysis, computer vision and computational intelligence. He has published more than 300 research papers in a number of leading journals and leading conferences including IEEE Trans. Image Processing, IEEE Trans. SMC, Pattern Recognition. He has received many distinguished awards including ICPR2012 Best Scientific Paper Award, 2014 JAMIT Best Paper Award, Outstanding Chinese Oversea Scholar Fund of Chinese Academy of Science. He is/was a leader of numerous national and industrial research projects.
Speech Title: "Weakly-supervised and Semi-supervised Deep Learning for Computer-aided Diagnosis and Surgery Support"
Abstract: Recently, deep learning (DL) plays important roles in many academic and industrial areas especially in computer vision and image recognition. Deep learning uses a neural network with deep structure to build a high-level feature space. It learns data-driven, highly representative, hierarchical image features, which have proven to be superior to conventional hand-crafted low-level features and mid-level features. In ILSVRC2015 (an Annual competition of image classification at large scale), higher recognition accuracy by deep learning than human has been achieved. Deep learning (DL) has also been applied to medical image analysis. Compared with DL-based natural image analysis, there are several challenges in DL-based medical image analysis due to their high dimensionality and limited number of labeled training samples. We proposed several weakly-supervised and semi-supervised deep learning techniques for computer-aided diagnosis and surgery support including medical image segmentation, medical image detection and medical image recognition. In this talk, I will talk about current progress and futures of computer-aided diagnosis and surgery support with deep learning.