New AI-powered deep learning model to support medical diagnostics

A new deep-learning model can learn to identify diseases from medical scans faster and more accurately, according to new research by a team of University of Alberta computing scientists and the U of A spinoff company MEDO. The breakthrough model is the work of a team of researchers in the Faculty of Science--including the contributions of Pouneh Gorji, a graduate student lost in Flight PS752. Deep learning is a type of machine learning--a subfield of artificial intelligence; deep learning techniques are computer algorithms that find patterns in large sets of data, producing models that can then be used to make predictions.These models work best when they learn from hundreds of thousands or even millions of examples. But the field of medical diagnostics presents a unique challenge, where researchers typically only have access to a few hundred medical scan images for reasons of privacy. "When a deep-learning model is trained with so few instances, its performance tends to be poor," said Roberto Vega, lead author of the study and graduate student in the Department of Computing Science.