Towards AI-Powered Healthcare: Automated Medical Image Analysis via Deep Learning

Speaker:        Dr. Qi DOU
                Department of Computing
                Imperial College London

Title:          "Towards AI-Powered Healthcare: Automated Medical Image
                 Analysis via Deep Learning"

Date:           Monday, 11 February 2019

Time:           4:00pm - 5:00pm

Venue:          Lecture Theater F (near lift 25/26),HKUST

Abstract:

In modern healthcare, disease diagnosis, assessment and therapy have been
significantly depending on the interpretation of medical images, e.g., CT,
MRI, Ultrasound, histology images and endoscopy surgical videos. The
exploding amount of biomedical image data collected in nowadays clinical
centers offer an unprecedented challenge, as well as enormous
opportunities, to develop a new-generation of data analytics techniques
for improving patient care and even revolutionizing healthcare industry.
In the meanwhile, the momentum in cutting-edge AI systems is towards
representation learning and pattern recognition via data-driven
approaches. In this talk, I will present a series of deep learning methods
towards interdisciplinary researches at artificial intelligence and
medical image analysis, for improving lesion detection, anatomical
structure segmentation and quantification, cancer diagnosis and therapy.
The proposed methods cover a wide range of deep learning topics including
design of network architectures, novel learning strategies, multi-task
learning, adversarial training, domain adaptation, etc. The challenges,
up-to-date progresses and promising future directions of AI-powered
healthcare will also be discussed.


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Biography:

Dr. Qi DOU is currently a postdoctoral research associate at the
Department of Computing at Imperial College London. Before that, she has
received her Ph.D. degree in Computer Science and Engineering at The
Chinese University of Hong Kong in July 2018, and was a postdoctoral
research fellow in the same lab for three months. She got her Bachelor's
degree in Biomedical Engineering at Beihang University in China with honor
in 2014. Her research interests are in the development of advanced machine
learning methods for medical image analysis, with expertise in deep
learning. She has won the Best Paper Award of Medical Image
Analysis-MICCAI in 2017, the Best Paper Award of Medical Imaging and
Augmented Reality in 2016, and MICCAI Young Scientist Award Runner-up in
2016. She has also won the CUHK Postgraduate Research Output Award 2017
and Best Paper Award of CUHK International Doctoral Forum 2016. She was
also the winner of Young Scientist Award at the Hong Kong Institution of
Science in 2018. She has published 30+ papers in top conferences and
journals on the topic of deep learning for medical data analysis. She
serves as Area Chair of MIDL'19, PC of IJCAI'19, AAAI'19, IJCAI'18,
Reviewer of journals such as IEEE-TMI, IEEE-TBME, IEEE-CYB, Medical Image
Analysis, Pattern Recognition, Neurocomputing.  Her current Google Scholar
citation has reached 1300+ with h-index 17.