To assist students in broadening their research perspectives, stimulating innovative thinking, and enhancing the quality of their papers, City University of Macau (CityU) organized an academic exchange workshop today at the Luso-Chinese Building, Taipa Campus. The workshop featured invited speakers from Tsinghua University, including Dr. Shi Diwei, a postdoctoral researcher, and Ph.D. student Chen Li, to share insights on the topic of “The Applications of Deep-Learning in Microscale Magnetic Resonance Imaging (MRI).” The workshop provided students with an opportunity to engage in open dialogue on cutting-edge research topics, thereby enhancing their academic literacy.
Professor Sun Pei from the Faculty of Health Sciences (FHS), Associate Professor James Ying Zuobin, Assistant Professors Wu Xiaofeng and Cui Sanshuai, along with postgraduate students from the Faculty of Data Science (FDS), attended the workshop.
At the beginning of the workshop, Chen Li delivered a presentation titled “Design and Application of Deep-Learning Algorithms in Interface Experiments at Microscale.” He discussed the potential demonstrated by deep-learning algorithms in interface experiments at microscale. Chen continued by introducing the applications of deep-learning in interface experiments, particularly in the context of “superhydrophobicity for solid-liquid interface” and “structural superlubricity for solid-solid interface.”
Subsequently, Shi Diwei delivered a talk titled “Pushing the Limits in Diffusion Magnetic Resonance Imaging: From Macro- to Microscale Research and Technological Development.” He identified the unique features of diffusion MRI as the sole non-invasive imaging method currently available for measuring the diffusion motion of water molecules within biological organisms. Additionally, Shi discussed research highlights and technological frontiers in the field of diffusion MRI, focusing on topics such as “whole-brain neural fiber tract tracing” and “microstructural detection of tumors.”
During the Q-and-A session, the speakers addressed students’ questions and expanded on real-world applications and cases, enabling students to develop a deeper understanding of deep learning in MRI at microscale.
Source: Faculty of Data Science
