Biological and Biomedical Engineering
Yujing (McMedHacks Co-director, Founder) was born and raised in the beautiful city of Tianjin, in China, 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 started her Ph.D. in the Biological & Biomedical Engineering (BBME) Department in 2022. 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 as an undergraduate researcher in 2018 and joined the Enger lab in 2021 during her M.Sc.. Her current research interests lie at the intersection of deep learning, image processing & analysis, and outcome prediction modelling in medical physics.
- 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.
- Treatment outcome Prediction for gynecological cancers patients with a multimodality deep learning model using pre/post diagnostic image modalities and digital histopathology images.
McMedHacks (Co-director, founder)
Publications / Awards
from The Institut TransMedTech in 2022.
ESTRO 2022: innovations in brachytherapy Journal Article
In: ESTRO Newsletter, pp. 754–767, 2022.
from Fonds de recherche du Québec in 2022.
from Fonds de recherche du Québec in 2022.
from Curietherapies in 2022.
from McGill Biological & Biomedical Engineering department in 2022.
from in 2022.
In: MEDICAL PHYSICS, pp. E266–E266, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.
In: Radiotherapy and Oncology, 170 , pp. S4–S5, 2022.
Treatment outcome Prediction for gynecological cancers patients with a machine learning model using pre/post diagnostic image modalities and digital histopathology images Presentation
CRUK RadNet Manchester AI for Optimising Radiotherapy Outcomes Workshop, 10.02.2021.
Graduate Excellence Fellowship Award
from McGill Medical Physics Unit in 2020.
- Y.Zou†*, L. Weishaupt, S.A.Enger, “McMedHacks: Deep learning for medical image analysis workshops and Hackathon in radiation oncology”, proffered paper, Track: Interdisciplinary, Session:Symposium – Education in radiation oncology: Advances and opportunities, European Society for Radiotherapy and Oncology
(ESTRO) conference, May 7th,2022, International 10 min Oral Presentation. [Congress schedule]
- Y.Zou†*, M. Lecavalier-Barsoum, M. Pelmus, S.A. Enger, “Investigation of cell spacing, and nuclei size distribution extracted from H&E histopathological Whole-Slide-Images for integration into a multimodality treatment outcome prediction model for gynecological cancer patients”, AQPMC Annual Scientific Meeting, Dec 3rd, 2021, 15 min Provincial Oral Presentation.
- Y.Zou†*, M. Lecavalier-Barsoum, M. Pelmus, S.A. Enger, “Deep Learning-Based Patient-Specific Multimodality Treatment Outcome Prediction for Gynecological
Cancers using pre/post diagnostic image modalities and digital histopathology images”, Friday Morning Talk, McGill Medical Physics Unit, October 1st, 2021, 17 min Institutional Oral Presentation. [schedule]
- Y.Zou†*, L.L.Weishaupt*, P.Watson*, “Ultrasound and instance segmentation”, July 10th, 2021, McMedHacks Workshop Week 5, 1.5-hour lecture. [link]
- Y.Zou†*, L.L.Weishaupt*, “Digital histopathological image analysis”, June 25th, 2021, McMedHacks Workshop Week 3, 1.5-hour lecture. [link]
- Y.Zou†*, L.L.Weishaupt*, “Introduction to Python for deep learning”, June 12th, 2021, McMedHacks Workshop Week 1, 1.5-hour lecture. [link]
- Y.Zou†*, M. Lecavalier-Barsoum, S.A. Enger, Treatment outcome Prediction for gynecological cancers patients with a machine learning model using pre/post diagnostic image modalities and digital histopathology images, CRUK RadNet Manchester AI for Optimising Radiotherapy Outcomes Workshop, VIRTUAL. February 10, 2021. Oral Presentation (1 min fire-up pitch).
- 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)
- 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)
- 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.
- Y.Zou†*, Mathematical Modelling Applications in Biology and Medicine, Seminars in Undergraduate Mathematics in Montreal (SUMM), Jan 13,2018, 25 min Oral Presentation.