Prof. Buyong Ma

Shanghai Jiaotong University, China


Professor Buyong Ma has more than 25 years experiences in the computational studies of protein structure and function, protein engineering, and antibody-antigen recognition. He has proposed “conformation selection” theory which is widely used in explaining drug-target interaction. Dr. Ma received his Ph.D. in physical chemistry from the University of Georgia at Athens in 1995 and continued his postdoctoral training in Professor Norman Allinger’s Lab working on computational chemistry. He joined NCI/NIH in 1998. In 2019, Dr. Ma accepted a tenured professor position in the school of pharmacy, Shanghai Jiaotong University, focusing on antibody drug design. He has published more than 200 papers, with google citation of 20848 and H-index 66.


Assoc. Prof. Aiping Yao

Lanzhou University, China


Dr. Aiping Yao is currently an associate professor in school of information science and engineering at Lanzhou University. She completed her master degree at Chinese Academy of Science and got her Ph.D from ETH Zurich. Before she joined Lanzhou University, she also worked at IT’IS Foundation as a project leader. Her research interest lies in the field of computational electromagnetic and bioelectromagnetics, especially in the electromagnetic safety evaluation for patients with implantable medical devices under magnetic resonance imaging (MRI).


Speech Title: "An Efficient Evaluation of RF Transfer Function of Active Implantable Medical Devices in Homogeneous and Heterogeneous Environment"


Abstract: Both the Tier-3 and Tier-4 methods proposed in ISO/TS 10974 for assessing the radiofrequency safety of active implantable medical devices have limitations in practice. The transfer function proposed in the Tier-3 method can only be measured individually for each implant and only for homogeneous tissue environments, while Tier-4 requires significant high-speed computing resources. In this study, machine learning algorithms are proposed to predict the transfer function both in homogeneous and heterogeneous tissue environment. The results show that this approach is feasible and performs well in the prediction task. This work will significantly reduce the computational resources required for RF safety assessment and greatly improve the efficiency of the assessment, as well as provide a more accurate result for implants with relatively complex implant environments.


Dr. Weimin Zhou

Shanghai Jiao Tong University, China


Dr. Weimin Zhou is a Tenure-Track Assistant Professor at the Global Institute of Future Technology at Shanghai Jiao Tong University (SJTU). Before joining SJTU in 2022, he was a Postdoctoral Scholar at University of California, Santa Barbara (UCSB). Dr. Zhou received his bachelor’s degree from Beijing University of Posts and Telecommunications (BUPT) in 2014 and his M.S. and Ph.D. degrees from Washington University in St. Louis (WUSTL) in 2016 and 2020. During his Ph.D., he worked as a Research Assistant in the Department of Biomedical Engineering at WUSTL and a Visiting Scholar in the Department of Bioengineering at University of Illinois at Urbana-Champaign (UIUC). His research focuses on medical imaging technologies, image science, and machine learning. Dr. Zhou possesses broad expertise in developing cutting-edge machine learning methods for addressing important needs in evaluating and optimizing medical imaging systems. His research has been published in leading medical imaging journals such as IEEE Transactions on Medical Imaging, Medical Physics, and Journal of Biomedical Optics. Dr. Zhou is the recipient of the SPIE Community Champion and the SPIE Cum Laude Award. He also serves as a program committee member of a leading medical imaging conference SPIE Medical Imaging.


Speech Title: "AI for Objective Assessment of Medical Imaging Systems"


Abstract: Medical imaging systems designed for specific tasks (e.g., tumor detection) should be objectively assessed via task-based measures of image quality (IQ). Task-based measures of IQ quantify the performance of an observer for specific tasks. They have been commonly used as figures of merit for guiding the evaluation and optimization of medical imaging hardware, software, and display devices. Virtual imaging trials and numerical observers are often employed when exploring and evaluating medical imaging systems. This talk will introduce some of my work on AI for addressing challenges in task-based assessment of medical imaging systems.










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