ECE Seminar - Towards AI-powered Healthcare: Computer-aided Diagnosis with Deep Learning
9:30am - 10:30am
ZOOM Seminar Details: https://hkust.zoom.us/j/91645885725?pwd=cXB5MlpwaEhBanhPNEZkdmI2ZUtGUT09, Meeting ID: 916 4588 5725

Abstract:


In the modern healthcare system, medical imaging technologies, such as CT, MRI, Ultrasound, histology images, fundus photography, play important roles in disease diagnosis, assessment, and therapy. Deep learning, a subfield of AI, has seen a dramatic resurgence in the recent few years, largely driven by increases in computational power and the availability of massive new datasets. Because of the increasing proliferation of medical devices and digital record systems, computer-aided diagnosis stands to benefit immensely from deep learning, which could aid physicians by offering second opinions and flagging concerning areas in images. In this talk, I will present a series of deep learning methods towards interdisciplinary researches at AI for medical image diagnoses, such as cancer classification and segmentation, anatomy tissue semantic parsing, and rare disease prediction. The proposed techniques cover a wide range of deep learning topics including neural network architecture design, semi-supervised and unsupervised learning, multi-task learning, few-shot learning, etc. The challenges, up-to-date progress, and promising future directions of AI-powered healthcare will also be discussed.

When
Time
9:30am - 10:30am
Where
ZOOM Seminar Details: https://hkust.zoom.us/j/91645885725?pwd=cXB5MlpwaEhBanhPNEZkdmI2ZUtGUT09, Meeting ID: 916 4588 5725
Event Format
Speakers / Performers:
Dr. Xiaomeng Li
Postdoctoral research fellow, School of medicine, Stanford University

Biography:

Dr. Xiaomeng LI is currently a postdoctoral research fellow at the School of medicine at Stanford University. Before that, she received her Ph.D. degree in the Department of Computer Science and Engineering at The Chinese University of Hong Kong. Her research interests are developing advanced machine learning/deep learning methods for medical image analysis, especially for medical image diagnosis. She won two international medical image diagnosis challenges as the main contributor. She published several papers in the top conferences and high impact journals in this area. Her first-authored paper “HDenseUNet” is listed among IEEE TMI Most Popular Articles in 2018, 2019, 2020 and is the “Highly cited paper” by ESI. She serves as a PC member of IJCAI’20, AAAI’20, CVPR’19 and MICCAI’19,20 workshops, and a reviewer of top journals and conferences such as IEEE-TMI, JBHI, Medical Image Analysis, ECCV’20, MICCAI’18,19,20, CVPR’20, ICCV’20, etc. Her current Google Scholar Citation reaches 500+ with h-index 7. Her released code has 340+ GitHub Star, with 120+ forks.

Language
English
Recommended For
Faculty and staff
PG students
Organizer
Department of Electronic & Computer Engineering
Contact
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