Medical Image Analysis and Reconstruction with Data-efficient Learning
08 Sep, 2021 (Wed)
11:00 am

Add to your calendar: iCal, Google Calendar

HKU Campus Map


Dr. Lequan Yu
Assistant Professor,
Department of Statistics and Actuarial Science, HKU


Medical imaging is a critical step in modern healthcare procedures. Accurate interpretation of medical images, e.g., CT, MRI, Ultrasound, and histology images, plays an essential role in computer-aided diagnosis, assessment, and therapy. While deep learning provides an avenue to deliver automated medical image analysis and reconstruction via data-driven representation learning, the success is largely attributed to the massive datasets with abundant annotations. However, collecting and labeling such large-scaled dataset is prohibitively expensive and time-consuming. In this talk, I will present our recent works on building data-efficient learning systems for medical image analysis and reconstruction, such as computer-aided diagnosis, anatomical structure segmentation, and CT reconstruction. The proposed methods cover a wide range of deep learning and machine learning topics, including semi-supervised learning, multi-modality learning, multi-task learning, integrating domain knowledge, etc. The up-to-date progress and promising future directions will also be discussed.


Join Zoom Meeting
Meeting ID: 936 4634 9367
Password: 886500


Biography of the speaker:

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 and Bachelor’s degree from Zhejiang University, both in Computer Science. He also experienced research internships in Nvidia and Siemens Healthineers.  His research interests are developing advanced machine learning methods for medical image analysis and reconstruction. He has won the CUHK Young Scholars Thesis Award 2019 and has also won the Best Paper Awards of Medical Image Analysis-MICCAI in 2017 and International Workshop on Machine Learning in Medical Imaging in MICCAI 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.


All are welcome!


Prof. Ed Wu

Most seminars are open to the general public, free of charge, unless otherwise stated. Registration is not required. Arrangement for car parking facilities on campus please contact us for details.

For enquiries, please contact:
Department of Electrical and Electronic Engineering,
Room 601, Chow Yei Ching Building,
Pokfulam Road, Hong Kong
Tel: (852) 3917 7093