Deep Learning in Medical Image Analysis: Methods, Applications and Beyond

Speaker:        Dr. Hao CHEN

Title:          "Deep Learning in Medical Image Analysis: Methods,
                 Applications and Beyond"

Date:           Monday, 9 November 2020

Time:           11:00am to 12 noon

Zoom web link:
https://hkust.zoom.us/j/94987739849?pwd=dEk1UzFTeEVDd2QxN3FjNW5HYmh6Zz09

Meeting ID:     949 8773 9849
Passcode:       201109

Abstract:

Deep learning represents data with multiple levels of abstraction and has
dramatically improved the state-of-the-art in many domains including
speech recognition, visual recognition and natural language processing.
Despite its breakthroughs in above domains, its application to medical
image analysis remains to be further explored. This talk will share our
studies on developing advanced deep learning methods and applications for
medical image analysis including robust three-dimensional deep learning
for high throughout volumetric image analysis, fusion of prior knowledge
and learning, semi-supervised or weakly deep learning for scalable
large-scale image analysis, adversarial learning for cross-domain
adaptation, etc., with an in-depth dive into predictive, diagnosis and
prognostic applications covering X-ray/CT/MRI/ultrasound in radiology, OCT
in ophthalmology and whole-slide image in pathology. To further unleash
the power of deep learning integrated into clinical scenarios, future
promises and pitfalls will also be discussed.


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Biography:

Dr. Hao Chen obtained Hong Kong PhD Fellowship in 2013 and received the
PhD degree from The Chinese University of Hong Kong (CUHK) in 2017. He
obtained the B.Eng. degree from Beihang University in 2013. He was a
postdoctoral research fellow in CUHK previously. His research interests
include medical image analysis, artificial intelligence and deep learning.
He has published 80+ papers in top-tier conferences and journals including
MIA, IEEE-TMI, CVPR, Radiology, Lancet Digital Health, NeuroImage, JAMA,
AAAI, IJCAI, MICCAI, etc. He received several premium awards including
Best Paper Award in MIAR 2016, CUHK Faculty Outstanding Thesis Award in
2017, MIA-Elsevier Best Paper Award in 2017, MICCAI Young Scientist
Publication Impact Award in 2019. He serves as the reviewer of a dozen of
premium conferences and journals including IEEE-TPAMI, Nature
Communications, MIA, IEEE-TMI, AAAI, etc. He also has industrial research
experience in Siemens and startup. During the last few years, the team he
led has won 15+ grand challenges on medical image analysis, such as LUNA
Challenge in ISBI, RSNA Challenge on Pneumonia Screening, etc.