Deep learning for health informatics: reviews, challenges, and opportunities on medical imaging, electronic health records, genomics, sensing and online health

PhD Qualifying Examination


Title: "Deep learning for health informatics: reviews, challenges, and 
opportunities on medical imaging, electronic health records, genomics, 
sensing and online health"

by

Miss Hyunjung KWAK


Abstract:

Machine learning and deep learning have provided us an exploration of a 
whole new research era. As more data and better computational power become 
available, their role has grown rapidly. In fact, these have been 
implemented in various fields and the demand for AI in the field of health 
informatics has increased, and the potential benefits of artificial 
intelligence applications in healthcare have also been proven. Deep 
learning combats lack of labelled data and burden on clinicians. In 
addition, it helps quickly diagnosing disease, locating cancer, predicting 
the spread of infectious diseases, and exploring new phenotypes with high 
accuracy. Therefore, It is expected to ultimately change human life a lot 
in the future. Despite its notable advantages, there are some challenges 
such as high dimensional, heterogeneous, time dependent, sparse and 
irregular data, lack of label data, model reliability, interpretability 
and practical use. This article presents a comprehensive review of 
research applying deep learning in health informatics over the last five 
years in the fields of medical imaging, medical informatics, genomics, 
sensing and online health, as well as challenges and promising directions 
for future research. We highlight ongoing popular approaches’ research and 
identify several challenges in building deep learning models.


Date:			Wednesday, 24 July 2019

Time:                  	4:30pm - 6:30pm

Venue:                  Room 3494
                         Lifts 25/26

Committee Members:	Dr. Pan Hui (Supervisor)
 			Prof. Nevin Zhang (Chairperson)
 			Prof. Gary Chan
 			Dr. Raymond Wong


**** ALL are Welcome ****