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Nov 29, 2021

Seminar (2021-11-29)

School of Biomedical Sciences is pleased to invite you to join the following seminar:

Date: Monday, 29 November 2021

Time: 4:00 pm – 5:30 pm

Venue: Cheung Kung Hai Lecture Theatre 2, G/F, William M.W. Mong Block, 21 Sassoon Road, Pokfulam, Hong Kong

Speaker: Dr. Lequan Yu, Assistant Professor, Department of Statistics & Actuarial Science, Faculty of Science, HKU

Title: “Deep learning meets medical imaging: Applications, challenges, and practices”



Dr. Lequan Yu is an Assistant Professor at the Department of Statistics and Actuarial Science, the University of Hong Kong. Before joining HKU, he was a postdoctoral fellow at Stanford University. He obtained his Ph.D. degree from The Chinese University of Hong Kong in 2019 and Bachelor’s degree from Zhejiang University in 2015, both in Computer Science. He also experienced research internships in Nvidia and Siemens Healthineers. His research interests are developing advanced machine learning methods for biomedical data analysis, with a primary focus on medical images. He has won the CUHK Young Scholars Thesis Award 2019, Hong Kong Institute of Science Young Scientist Award shortlist in 2019, Best Paper Awards of Medical Image Analysis-MICCAI in 2017 and International Workshop on Machine Learning in Medical Imaging in 2017. He serves as the senior PC member of IJCAI, AAAI, and the reviewer for top-tier journals and conferences, such as Nature Machine Intelligence, IEEE-PAMI, IEEE-TMI, Medical Image Analysis, etc. His current Google Scholar citation has reached 6100+ with h-index 35.


Medical imaging is a critical step in modern healthcare procedures. Artificial Intelligence (AI), especially deep learning, has emerged as a key technology for developing novel tools in interpreting medical images, e.g., CT, MRI, ultrasound, histology images, and fundus images. In this talk, I will share our works on developing advanced deep learning methods for medical image analysis, such as computer-aided diagnosis, anatomical structure segmentation, whole-slide histology image analysis, and CT reconstruction. The proposed methods cover a wide range of deep learning topics, including label-efficient learning, multi-modality learning, and integrating domain knowledge, with applications in various clinical domains such as radiology, pathology, cardiology, and ophthalmology. The up-to-date progress and promising future directions will also be discussed.



Should you have any enquiries, please feel free to contact Miss River Wong at 3917 9216.