Accenture estimated that artificial intelligence (AI) could save the healthcare industry $150 billion annually by 2026. Despite the great promise, AI models’ development and deployment face a significant challenge of data heterogeneity. Specifically, AI models trained on data from one hospital will likely suffer from accuracy drops (biases) when performing on data coming from another hospital. For example, an algorithm developed at a hospital with a mostly Caucasian patient population will not achieve the same accuracy on minority patients from a local clinic. Meta-learning is a promising framework to mitigate the above-mentioned problem. In this talk, I will give a brief introduction to contemporary meta-learning algorithms, and show their applications to biomedical image analysis.
Dr. Hien Van Nguyen is an Assistant Professor of the Department of Electrical and Computer Engineering Department at the University of Houston. He received his B.S. degree from the National University of Singapore in 2007 and a Ph.D. degree from the University of Maryland in 2013. Dr. Nguyen has published 50 peer-reviewed papers and received 12 U.S. patents. He is passionate about inventing novel algorithms to address pressing challenges in healthcare and medical diagnosis. His research has been funded by NSF and NIH, and featured by the Computing Research Association in the Great Innovative Ideas series. Before UH, he was a senior research scientist at Uber Autonomous Driving division (Pittsburgh, PA), and a scientist at Siemens Corporate Research (Princeton, NJ). He is a senior member of the National Academy of Inventors.