Yujing Zou

Current: M.Sc. Candidate in Medical Physics at McGill University
Dr. Shirin A. Enger’s Artificial Intelligence Group

B.Sc. in Joint major: Mathematics & Physiology, Minor: Physics at McGill University

+1 (514) 961-2768

Email:yujing.zou@mail.mcgill.ca

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Music Production: Covers & Original demos recorded, produced, mixed and mastered using Reaper. Ex.:

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Bio

Yujing (McMedHacks Co-director, Founder) was born and raised in the beautiful city of Tianjin before moving to Regina, Saskatchewan, in Canada and went to Balfour Collegiate. After graduating from a joint major in physiology and mathematics with a minor in physics at McGill University in 2020 in Montreal, Quebec, she joined the CAMPEP-accredited Medical Physics M.Sc. program at McGill and has recently completed the first-year courses. Throughout her degrees, she has been inspired and drawn to interdisciplinary research where mathematical modelling and computational tools are used to uncover problems in medicine. She joined the McGill Medical Physics Unit (MPU) as an undergraduate researcher in 2018 and has recently joined the Enger lab in 2021. Her current research interests lie at the intersection of deep learning, image processing & analysis, and outcome prediction modelling in medical physics.

Current Research Interests / Projects (Supervisor: Dr. Shirin A. Enger)

1. Correlation between microscopic influence of cell spacing and
   nuclei size, extracted from Hematoxylin and Eosin (H&E) stained digital histopathological images, on treatment outcomes in radiation therapy.
2. Treatment outcome Prediction for gynecological cancers patients with a multimodality deep learning model using pre/post diagnostic image modalities and digital histopathology images.

My current research interests 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.

Selected Presentation, Conferences, Publications

1. A. Diamant†, A. Chatterjee, M. Vallières, M. Serban, Y. Zou†*, R. Forghani, G. Shenouda, J. Seuntjens, Multi-modal deep learning framework for head & neck cancer outcome prediction, Annual meeting of the McGill Initiative in Computational Medicine (MiCM), VIRTUAL, November 27, 2020. Presentation. (Video) (MiCM Summer Scholar 2020 Profile)
2. Y. Zou†*, G. Bub, Comparison of complexity and predictability of a cellular automaton model in excitable media cardiac wave propagation compared with a FitzHugh-Nagumo model, McGill Science Undergraduate Research Journal, 66-71, 2019-2020 issue. (Paper on Google Scholar, ResearchGate) (McGill Undergraduate Physiology Research day, March 29, 2019. Poster)
3. Y.Zou†*, A. Chatterjee, J. Seuntjens, A radiomics study: Comparison of lymphadenopathy disease outcomes prediction power between 2D and 3D features fromhead and neck cancer dual-energy CTs, RI-MUHC Summer Student Research Day, August 13, 2018. Poster Presentation.
4. Y.Zou†*, Mathematical Modelling Applications in Biology and Medicine, Seminars in Undergraduate Mathematics in Montreal (SUMM), Jan 13,2018, 25 min Oral Presentation.

Selected Leadership Experiences

McMedHacks (Co-director, founder)
(2021 – Present)