Throughout history advancements in technology have played a significant role in how we live our lives. It has continuously aided in healthcare breakthroughs and holds significant potential for the future. Researchers at Sunnybrook are using emerging artificial intelligence (AI) technologies to overcome some of health care’s most complex challenges, like the manual analysis of complex medical images.
Tony Xu, PhD student in the Department of Medical Biophysics at the University of Toronto, was awarded the 2024 Google PhD Fellowship in Health & Bioscience. The Google PhD Fellowship is a highly competitive program that recognizes outstanding graduate students doing exceptional and innovative research in areas relevant to computer science and related fields, providing mentorship and funding to advance their work. Tony is working at Sunnybrook Research Institute (SRI) with Dr. Anne Martel, senior scientist in the Odette Cancer Research Program and Dr. Maged Goubran, scientist in the Hurvitz Brain Sciences Research Program. His research at SRI focuses on using self-supervised learning (SSL) to tackle key challenges associated with analyzing large and multidimensional medical images.
Medical images, like whole-slide histopathological images and light-sheet fluorescence microscopy images, have incredibly high resolution to resolve microscopic, cellular detail so clinicians can gain insights to discover complex biomarkers for illness. However, given the large size and density of information displayed in these images, manual analysis can be both time-consuming and difficult for the human eye.
“My research looks to broadly improve on traditional deep-learning methods to speed up the analysis of medical images used by other researchers, and to bring deep-learning closer to clinical application,” explains Tony. “My research has the potential to help researchers analyze data faster and with less-effort, increasing foundational research and helping it reach clinical applications.”
Deep learning is an AI method that recognizes complex data to produce accurate predictions and insights, inspired by the way the human brain would. Many traditional deep learning methods are heavily dependent on expert annotations, or labeling individual elements in data sets to help machines understand the contents, which can take up to weeks to create for just a single image. Tony’s research with SSL works by training deep learning models on unannotated images to teach it to recognize and predict missing data, significantly decreasing the need for expert input.
“Data in the medical domain is not only becoming increasingly available, but also increasingly dense,” adds Tony. “New methods that are able to learn to extract information and patterns from raw data, can play an invaluable role in creating powerful, generalizable models that can be trained just once and applied to a multitude of clinical tasks.”
AI research like Tony’s has the potential to dramatically improve the workflow of researchers and clinicians alike, impacting patient outcomes by assisting clinicians with analyzing images and diagnosing disease and promoting more fundamental health care research.
