James Tsui

Professional MSc in Machine Learning, Computer Science
BEng, MD, CM, PhD, FRCPC
Artificial Intelligence Group
Learn more

Bio

James completed his Bachelor Degree in Electrical Engineering in the Co-op program at Concordia University in 2006. His interest in the human mind later led him to pursue a PhD in computational neuroscience at the Montreal Neurological Institute / McGill University and defended his thesis in 2013. His work entailed building descriptive and predictive mathematical models to help explain phenomena underlying conscious perception of motion. It was while working in the hospital setting during his graduate studies that exposed him to medicine. This led him to complete his MD degree in 2015, and residency training in Radiation Oncology in 2020 at McGill University. After graduation, he joined the Radiation Oncology Department as practicing physician at the Royal Victoria Hospital, and was appointed Assistant Professor in Clinical Oncology at McGill. To further his sub-specialization training, he recently completed a Brachytherapy Fellowship at the Brigham and Women’s Hospital, Harvard, Boston, 2021, and is currently completing his Professional Master Degree in Artificial Intelligence at MILA / Université de Montréal.

His interests are in the intersection of machine learning and medicine, with the goal to bring artificial intelligence into his oncology research and clinical practice.

Current Projects

Auto-segmentation of brain structures

Radiation therapy to brain tumor requires a delicate balance between delivering adequate tumorcidal dose to the tumor lesion while minimizing radiation to healthy organs. Segmentation of organs at risk (OARs) is time-consuming and subject to inter-observer variability. Auto-segmentation can 1) increase workflow output by lightening segmentation time, and 2) decrease inter-observer variability. The Radiation Oncology Department at the McGill University Health Centre in collaboration with the Montreal Children Hospital treat pediatric patients with brain tumors. Compared to adult population, radiation therapy to pediatric population is higher stake for several reasons.1) As opposed to adult patients with mature anatomy, radiation can several impair normal growth of pediatric patients; 2) There are more brain structures in pediatric patients to consider; 3) Most children brain structures are more sensitive to radiation than those of adults. Thus, accurate segmentation is of great importance. The project is to investigate deep learning algorithms that can accurately auto-segment OARs in pediatric patients undergoing brain radiation therapy.