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Hello, my name is Yujing! I am currently a masters student at the McGill medical physics unit. Currently in Dr. Shirin Enger’s lab, my research interests lie in the intersection of deep learning, image processing & analysis, and outcome prediction modelling in medical physics.
I recently finished my first year of the CAMPEP-accredited Medical Physics M.Sc. program at McGill University. In 2020, I graduated from a joint major in physiology and mathematics with a minor in physics also at McGill. Throughout my degrees, I have been inspired and drawn to interdisciplinary research where mathematical modeling and computational tools are used to uncover problems in medicine. My current research interest surround the topic of deep learning-based outcome prediction modelling using multiple diagnostic imaging modalities such CT, MRI, histopathological images and Ultrasound. More specifically, despite combination of different treatment modalities, such as radiotherapy including brachytherapy, surgery, chemotherapy, chemoradiotherapy, and improvement of treatment protocols and imaging techniques used for management of cancer, it remains clinically challenging to predict which patients will benefit from which treatment combination. An accurate method for predicting a patient’s likelihood of response could reduce unnecessary interventions, lower healthcare costs, and reduce side effects. This prompted our objective to examine how pre-treatment patient characteristics, through imaging data, influence treatment efficacy as measured by post-treatment response, therefore to build a treatment outcome prediction model.
Previously during my undergraduate studies, I have also worked on non-linear dynamics problems in biology, as well as Radiomics and auto-segmentation algorithms on medical images. For example, 1) I investigated a type of discrete mathematical model called Cellular Automata (CA) model and compared its predictability in real-time excitable media cardiac cell wave propagation data with a continuous model called FitzHugh-Nagumo (FHN) model in Dr. Gil Bub’s lab. A Convolutional Neural Network (CNN) model integrated with the CA is developed to increase the model’s predictability at higher time steps. 2) Radiomics is a field examining correlations between diagnostic image features and treatment outcomes in radiation oncology. In Dr. Jan Seuntjens’s lab, I compared lymphadenopathy outcome prediction power between 2D (i.e. central tumor slice image) and 3D (i.e. whole tumor volume images) features from head and neck cancer dual-energy CTs (DECT) data at 21 energy levels from 87 patients’ DECT scans, and understanding how energy levels benefit different feature types.
Outside of academics, I am extremely passionate about songwriting and music production technologies, as well as learning new programming tricks and languages. My favourite band right now is Vulfpeck!
I am beyond excited to organize and lead this workshop series & Hackathon with my colleagues! I hope to see many of you soon!