Donald Y. C. Lie received his B.S.E.E. degree from the National Taiwan University in 1987, and the M.S. and Ph.D. in electrical engineering (minor in applied physics) from Caltech, Pasadena, in 1990 and 1995, respectively. He held technical and managerial positions at companies such as Rockwell International, Silicon-Wave (now Qualcomm), IBM, Microtune, and is currently the Keh-Shew Lu Regents Chair Professor in the Department of Electrical and Computer Engineering, Texas Tech University, and also an Adjunct Professor in the Department of Surgery, Texas Tech University Health Sciences Center. He was a Visiting Lecturer to the ECE Department, University of California, San Diego (UCSD) during 2002-2007 and co-supervised Ph.D. students. He and his students have won 16 Best Paper Awards and authored 220 peer-reviewed technical papers and book chapters with three TOP 100 most downloaded papers in IEEE Xplore™ and he holds seven U.S. patents. Dr. Lie has been awarded with 6 DARPA contracts, and appointed as a Chair Professor, College of Electrical Engineering, National Chiao-Tung University (NCTU), Hsin-Chu, Taiwan, since 2018. He is a Fellow of IEEE. His research interests are: (1) power-efficient 5G/6G mm-Wave/RF/Analog IC design; and (2) interdisciplinary/clinical research on medical electronics, biosensors, oncology, and AI-assisted medicine.
Speech Title:"Prevent the Spread of COVID-19 with Digital Health and Smart Biosensors"
Abstract: The concept of “Digital Health” covers broad areas such as mobile health (mHealth), health information and communication technology (ICT), wearable devices, telehealth and telemedicine, and personalized medicine. Digital health typically involves both software and hardware, utilizing mobile phone or sensor technologies to deliver and/or to improve patient’s healthcare. In 2019, the FDA in the US published a Digital Health Innovation Action Plan, as digital health can empower consumers, healthcare providers and even government agencies to make better-informed decisions during public health crisis, and provide new options for prevention, early diagnosis of life-threatening diseases, and management of chronic conditions. As COVID-19 is highly contagious and the confirmed cases already surpassed 8.8 million with over 230,000 deaths in the US alone, we would like to discuss a real-life example in a country where digital health has been very instrumental to have stopped the spread of COVID-19 without ever needing to lockdown. The COVID-19 death rate in this country is less than 0.3 per million, ranked 192th lowest among 215 countries in the world. We will discuss her digital health strategies on big data analytics, border control and tracking, contact tracing, etc. We will also showcase several novel biosensor technologies our group have developed that might help advance digital health in the post-COVID-19 era. We strongly suggest that there should be urgent efforts for each country to carefully look into the development and deployment of various digital health tools to combat COVID-19.
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: "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.