Assoc. Prof. Lee Poh Foong

Universiti Tunku Abdual Rahman, Malaysia


Dr. Lee Poh Foong obtained her bachelor's degree in industrial physics, master's degree in plasma physics, and PhD in the field of biophysics from the University of Malaya. Her passion for physics and its application in solving biological problems has been a driving force since her early years. In recent times, her research has been primarily focused on utilizing neuroscience and biophysics approaches to enhance the well-being of the body and mind. Currently, Dr. Lee holds the position of associate professor at the University Tunku Abdul Rahman. Her expertise and accomplishments have been widely recognized, as she has been honored as a Chartered Engineer by the Institute of Engineering and Technology, UK, and as a Professional Technologist in Malaysia. Additionally, she has taken the initiative to establish the Cognitive Ergonomy Society Malaysia, aiming to contribute various methods and tools for optimizing cognitive levels
among different populations.


Speech Title: "Enhanced Conflict Monitoring via a Short-duration, Video-assisted Deep Breathing in Healthy Young Adults: An Event-related Potential Approach through the Go/NoGo Paradigm"


Abstract: This study investigates the impact of short duration deep breathing on cognitive control, using guided videos of 5, 7, and 9 minutes. The effects were measured through a Go/NoGo task and event-related potential (ERP) at specific brain locations. Results showed that participants in the 5-minute deep breathing group had significantly improved conflict monitoring ability, as indicated by increased NoGo N2 amplitude, compared to the control group. An inverse relationship was found between breathing duration and NoGo N2 amplitude. The study suggests that short duration deep breathing can enhance conflict monitoring, with 5 minutes being the optimal duration. It's the first to link deep breathing with conflict monitoring through ERP, using a young adult population to minimize variance. Limitations include the need for a larger sample size and further research on longer breathing durations and gender-specific analysis.



Assoc. Prof. Lv Meng

Beijing Institute of Technology, China


Dr. Lv Meng is an associate researcher at Beijing Institute of Technology. Her research focuses on intelligent interpretation and application research of medical hyperspectral images, and has developed a prototype of disease intelligent assisted diagnosis principles based on hyperspectral imaging technology, which has potential clinical application value and strong potential for scientific and technological achievements transformation. She has led the National Natural Science Foundation of China (Youth Project) and the International Cooperation Project of the Ministry of Science and Technology, participated in multiple national and provincial level projects, and authorized multiple invention patents. Published over 10 SCI papers as the first/corresponding author in IEEE JBHI, BOE, and other journals.


Speech Title: "The Application and Challenges of Hyperspectral Imaging Technology in Clinical Auxiliary Diagnosis"


Abstract: Hyperspectral imaging technology endows medical images with the characteristic of "spectral integration", which can finely resolve and translate different types of targets. It has great potential for development in modern medical fields such as disease screening, non-contact cell screening, and disease diagnosis. Hyperspectral imaging, as an emerging technology in biomedical visualization, is gradually receiving attention in the field of biomedical research. The report focuses on hyperspectral medical image processing and applications, and proposes the future development direction of this technology in the field of biomedical research. It mainly introduces the principles, potential application directions, challenges and solutions of hyperspectral imaging.


Assoc. Prof. Ming Huang

Nagoya City University, Japan


Dr. Ming Huang earned his Ph.D. in Computer Science and Engineering from the University of Aizu in 2012. He is currently serving as an Associate Professor at Nagoya City University and as an Affiliate Associate Professor at the Nara Institute of Science and Technology. In addition, he is a visiting scholar in the Department of Biomedical Engineering at the University of California, Davis. Dr. Huang's research focuses on digital health using big data analytics, machine learning, and chemo/bioinformatics in biomedical engineering. Through his work, he has made substantial contributions to the advancement of next-generation personal healthcare. He achieved this by designing and validating the non-invasive deep body thermometry (DHFM), developing computational models for cuff-less blood pressure measurements using ECG and PPG signals, and integrating novel sensing technology and state-of-the-art machine learning models in personal sleep care.He has authored over 30 peer-reviewed papers in journals indexed by SCI and is presently an active reviewer for multiple prestigious journals, including the IEEE Journal of Biomedical and Health Informatics and the IEEE Transactions on Biomedical Engineering. Additionally, he is serving as the corresponding editor for several SCI journals such as Frontiers in Physiology and Sensors.


Speech Title: "Unlocking Sleep's Secrets: Transforming Sleep Stage Classification with Deep Learning"


Abstract: Sleep disorders can significantly impair cognitive functions, mood, and overall health, increasing the risk of chronic diseases such as cardiovascular problems, obesity, and diabetes. Accurate sleep staging is foundational to understanding sleep quality and diagnosing disorders, providing insights into the intricate dynamics of sleep. This research investigates deep learning applications for sleep stage classification, utilizing the electroencephalogram's (EEG) time-frequency properties to enhance data representation. A novel methodology that merges spectrogram analyses with Transformer models is introduced, significantly outperforming existing methods and establishing a new benchmark. Additionally, this work enhances interpretability by visualizing the model's decision-making, offering a method that aligns closely with sleep medicine principles and expert consensus.


Dr. Weimin Zhou

Shanghai Jiao Tong University, China


Weimin Zhou, PhD 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 in the Department of Psychological & Brain Sciences at the University of California, Santa Barbara (UCSB). Dr. Zhou received his Ph.D. degree in Electrical Engineering from Washington University in St. Louis (WashU) in 2020. During his Ph.D. study, he worked as a Research Assistant in the Department of Biomedical Engineering at WashU and a Visiting Scholar in the Department of Bioengineering at the University of Illinois Urbana-Champaign (UIUC). He possesses broad expertise in image science, computational image formation, visual perception, and machine learning. Dr. Zhou is the recipient of the SPIE Community Champion Award and the SPIE Medical Imaging Cum Laude Award. He serves as a Program Committee Member for SPIE Medical Imaging, an Area Chair for the Conference on Health, Inference, and Learning (CHIL), and a peer reviewer for a variety of medical imaging journals.


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


Abstract: Modern medical imaging systems rely on complicated hardware and sophisticated computational methods to produce diagnostically useful images. Because of the great number of system parameters that can affect image quality, the large variety in objects to be imaged, and ethical limitations, it is often impractical to assess imaging systems via clinical imaging trials, which can hinder the development of emerging imaging technologies. Due to these reasons, there has been growing interest in virtual imaging trials (VITs) that can emulate the clinical imaging process and permit the automated analysis of medical images in silico. However, effective VITs rely on two critical components: stochastic object models (SOMs) that capture realistic anatomical variations, and figures-of-merit that relate to the ability of an observer to perform specific clinical tasks in images. Establishing realistic SOMs and computing task-based measures for such VITs present significant challenges. Generative artificial intelligence (AI) is rapidly increasing its profound impact on medical imaging. In this talk, I will present deep generative models for establishing realistic SOMs from imaging measurements. I will also describe a Markov-Chain Monte Carlo (MCMC) method empowered by generative adversarial networks (GANs), referred to as MCMC-GAN, for approximating the Bayesian Ideal Observer for computing task-based measures of image quality.











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