Prof. Ce Zhu
IEEE/Optica/IET/AAIA Fellow
University of Electronic Science and Technology of China, China

Ce Zhu has been with University of Electronic Science and Technology of China (UESTC), Chengdu, China, as a Professor since 2012, and serves as the Dean of Glasgow College, a joint school between the University of Glasgow, UK and UESTC, China. His research interests include video coding and communications, video analysis and processing, 3D video, visual perception and applications. He has served on the editorial boards of a dozen 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, an Editor of IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, and an Area Editor of SIGNAL PROCESSING: IMAGE COMMUNICATION. He has also served as a Guest Editor of multiple special issues in international journals, including as a Guest Editor in the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING.
Prof. Zhu is an IEEE/Optica/IET/AAIA Fellow. He serves as the Chair of IEEE ICME Steering Committee (2024-2025). He was an IEEE Distinguished Lecturer of Circuits and Systems Society (2019-2020), and also an APSIPA Distinguished Lecturer (2021-2022). He is a co-recipient of multiple paper awards at international conferences, including the most recent Best Demo Award in IEEE MMSP 2022, and the Best Paper Runner Up Award in IEEE ICME 2020.

 

 

 

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.
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. Medical Imaging, CVPR, ICCV, MICCAI. He has received many distinguished awards including ICPR2012 Best Scientific Paper Award, 2014 JAMIT Best Paper Award. He is/was a leader of numerous national and industrial research projects. Professor Yen-Wei Chen is ranked in the World’s top 2% of scientists for both the single recent year (2023) and career-long (updated until to end-of-2022), according to Stanford/Elsevier's rankings.

 

 

 

Jun Cheng (Principal Scientist)
Agency for Science, Technology and Research (A*STAR), Singapore

Jun Cheng received the B. E. degree in electronic engineering and information science from the University of Science and Technology of China, and the Ph. D. degree from Nanyang Technological University, Singapore. He is now a principal scientist in the Institute for Infocomm Research, A*STAR, working on AI for medical imaging, robust vision & perception, and machine learning. He has authored/co-authored over 200 publications at prestigious journals/conferences, such as TMI, TIP, TBME, IOVS, JAMIA, MICCAI, CVPR and invented more than 20 patents. He has received the IES Prestigious Engineering Achievement Award 2013. He serves as reviewers for many journal/conferences and area chairs for MICCAI, AAAI, ICLR, NeurIPS. He is currently associate editor for IEEE IEEE TMI and Senior Area Editor for TIP.

Speech Title: Improving OCTA Imaging through Cross-Domain Adaptation: A Noise-Guided Framework Using Intralipid-Enhanced and High-overlapping Rat Data

Abstract: AI based Deep learning has been introduced into optical coherence tomography angiography (OCTA) imaging, which is a non-invasive technique for visualizing vascular structures. Intralipid injection and high-overlapping scanning have shown promise in improving blood cell scattering for better OCTA imaging. However, administering intralipid to human subjects for imaging purposes may raise ethical concerns while the high number of overlapping leads to long scanning duration and therefore large motion artefacts. To address this challenge, we acquire intralipid-enhanced high overlapping OCTA in rats and introduce cross-domain learning to address the domain shifts. Specifically, we collect data from eyes of anesthetized rats to obtain motion-free data and introduce a noise-guided self-training framework to bridge the domain gaps between rats and primates. Additionally, an en face enhancement loss is incorporated to further refine en face vectors during adaptation. Compared with other classical and fully supervised OCTA imaging algorithms, our method improves B-scan denoising performance significantly.