Yujing ZouMcGill University | McGill · Biological & Biomedical Engineering
Yujing Zou
Master of Science
About
5
Publications
335
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Citations since 2017
Introduction
I am a Ph.D. student in the Medical Physics Unit (& BBME) at McGill University in Dr. Shirin Abbasinejad Enger's lab. I am interested in outcome prediction modelling using multimodality medical images with deep learning techniques in radiation oncology and medical physics. Meanwhile, we founded a free and international summer school, McMedHacks, teaching medical image analysis using deep learning in Python. I also studied cardiac dynamics along with a Cellular Automaton model during undergrad.
Education
September 2020 - August 2022
September 2015 - April 2020
Publications
Publications (5)
Purpose/Objective:
The McMedHacks workshop and presentation series was created to teach individuals from various backgrounds about deep learning (DL) for medical image analysis in May, 2021.
Material/Methods:
McMedHacks is a free and student-led 8-week summer program. Registration for the event was open to everyone, including a form to survey pa...
Purpose:
To build a machine-learning (ML) classifier to predict the clinical endpoint of post-Radiation-Therapy (RT) recurrence of gynecological cancer patients, while exploring the outcome predictability of cell spacing and nuclei size pre-treatment histopathology image features and clinical variables.
Materials and Methods:
Thirty-six gynecolog...
ESTRO 2022 started on 6 May and came to a wonderful end on 10 May, 2022, in Copenhagen, Denmark. It was exciting to return to an on-site event to share recent work, research, and experiences with colleagues and friends. This year's meeting of the European SocieTy for Radiotherapy and Oncology (ESTRO) involved enriching presentations that encompasse...
Background: Excitable media are spatially distributed systems that propagate signals without damping. Examples include re propagating through a forest, the Belousov-Zhabotinsky reaction, and cardiac tissue. (1) Excitable media generate waves which synchronize cardiac muscle contraction with each heartbeat. Spa-tiotemporal patterns formed by excitat...
Network
Projects
Projects (2)
To develop a deep learning-based patient-specific multimodal treatment outcome prediction model and investigate how pretreatment patient characteristics of various data types influence treatment efficacy as measured by post-treatment response. Specific Aim A: Investigate the correlation between microscopic influence of cell spacing and nuclei size, extracted from Hematoxylin and Eosin(H&E) stained digital histopathological images, and gynecological cancer treatment outcomes (i.e., recurrence, toxicity, survival) in radiotherapy (external radiotherapy and brachytherapy).Specific Aim B: Developing a DL-based patient-specific treatment outcome prediction model that combines auto-segmented tumor radiomic signatures from diagnostic images (e.g., CT, MR, US) and digital histopathological images such as nuclei size & cell spacing distribution data, which will accurately predict outcomes for various treatment modality combinations for gynecological cancer patients.
Comparison of Complexity and Predictability of a Cellular Automaton Model in Excitable Media Cardiac Wave Propagation Compared with a FitzHugh-Nagumo Model
