Department of Computer Science HKBU
Invited Talk

COVID-Net: Open Source Deep Learning Initiative for COVID-19 Detection from Chest Radiography

Dr. Alexander Wong

Associate Professor
Department of Systems Design Engineering
University of Waterloo, Canada

Title: COVID-Net: Open Source Deep Learning Initiative for COVID-19 Detection from Chest Radiography
Date & Time: 09:00, 19 June 2020 GMT +8 (Hong Kong Time)
21:00, 18 June 2020 GMT -4 (Waterloo, Canada Time)
Registration link: https://bit.ly/reg-pg-20
(The talk will be conducted via ZOOM.)
Registration deadline: 16 June 2020
ABSTRACT

The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a global deep learning initiative for COVID-19 detection and risk stratification from chest radiography that is open source and available to the general public. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases. Furthermore, we investigate how the COVID-Net models makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net models in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. The hope is that the open source COVID-Net initiative will aid both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.

BIOGRAPHY

Alexander Wong, P.Eng., is currently the Canada Research Chair in Artificial Intelligence and Medical Imaging, Member of the College of the Royal Society of Canada, co-director of the Vision and Image Processing Research Group, and an associate professor in the Department of Systems Design Engineering at the University of Waterloo. He has published over 500 refereed journal and conference papers, as well as patents, in various fields such as computational imaging, artificial intelligence, computer vision, and multimedia systems.

In the area of computational imaging, his focus is on integrative computational imaging systems for biomedical imaging (inventor/co-inventor of Correlated Diffusion Imaging, Compensated Magnetic Resonance Imaging, Spectral Light-field Fusion Micro-tomography, Compensated Ultrasound Imaging, Coded Hemodynamic Imaging, High-throughput Computational Slits, Spectral Demultiplexing Imaging, and Parallel Epi-Spectropolarimetric Imaging). In the area of artificial intelligence, his focus is on operational artificial intelligence (co-inventor/inventor of, Generative Synthesis, evolutionary deep intelligence, Deep Bayesian Residual Transform, Discovery Radiomics, and random deep intelligence via deep-structured fully-connected graphical models).

He has received numerous awards including three Outstanding Performance Awards, a Distinguished Performance Award, an Engineering Research Excellence Award, a Sandford Fleming Teaching Excellence Award, an Early Researcher Award from the Ministry of Economic Development and Innovation, and over 20 research awards including a Best Paper Award at the NIPS Workshop on NIPS Workshop on Transparent and Interpretable Machine Learning (2017), a Best Paper Award at the NIPS Workshop on Efficient Methods for Deep Neural Networks (2016), and Synaptive Best Medical Imaging Paper Award (2016).

 

How Computer Science and AI Can Help Save the World from Deadly Infectious Diseases

Prof. Jiming Liu

Chair Professor
Department of Computer Science
Hong Kong Baptist University, Hong Kong

Title: How Computer Science and AI Can Help Save the World from Deadly Infectious Diseases
Date & Time: 14:00, 19 June 2020 GMT +8 (Hong Kong Time)
Registration link: https://bit.ly/reg-pg-20
(The talk will be conducted via ZOOM.)
Registration deadline: 16 June 2020
ABSTRACT

In this talk, Prof. Jiming Liu will share some of their stories in a journey of translating exciting computer science research into socially impactful solutions in infectious disease control and prevention, e.g., in combating the outbreaks of malaria, swine flu, and COVID-19. He will discuss their ongoing interdisciplinary efforts in developing and deploying Data Science and AI technologies for addressing some of the most pressing needs in global health, while envisioning grand computational and epidemiological challenges, as well as opportunities, ahead.

BIOGRAPHY

Prof. Jiming Liu has served as Chair Professor in Computer Science at Hong Kong Baptist University since 2010, where he also directs Centre for Health Informatics, as well as Joint Research Laboratory for Intelligent Disease Surveillance and Control (a research partnership with Chinese Center for Disease Control and Prevention). He received his MEng and PhD from McGill University, and has been a Fellow of the IEEE since 2011. His current research interests include Data-Driven Modeling, Machine Learning, Complex Networks, Web Intelligence, Autonomy-Oriented Computing, and AI for Social Good. He has served as Editor-in-Chief of Web Intelligence Journal, and Associate Editor of Big Data and Information Analytics, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cybernetics, and Computational Intelligence, among many others.