Yujing Zou

Ph.D. Student
Biological and Biomedical Engineering

Bio

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.

Current Projects

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.

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.

McMedHacks (Co-director, founder)

McMedHacks is a free international 8-week workshop series on medical imaging analysis using deep learning in Python from June 12th – July 31st, 2021, followed by a Hackathon in August. With Dr. Enger’s support, McMedHacks has generated registrations from 356 participants ranging from undergraduates, Masters, PhDs to MDs from 38 countries! Every weekend, inspirational speakers from academia and industry working at the intersection of medical imaging and deep learning are invited to present their cutting-edge work, followed by a hands-on interactive coding workshop led by the McMedHacks team and invited instructors. McMedHacks leads and fosters a passionate and collaborative scientific spirit in their team of 30 + members of internationally renowned speakers, instructors, mentors, and their own leadership teams (ex. sponsorship, hackathon, social media, mentorship, content development) from the Enger lab.

Publications / Awards

2022

Zou, Yujing

TransMedTech Excellence Scholarship (Doctoral Award) Award

from The Institut TransMedTech in 2022.

Links | BibTeX

Zou, Yujing; Alvarez, David-Santiago Ayala

ESTRO 2022: innovations in brachytherapy Journal Article

In: ESTRO Newsletter, pp. 754–767, 2022.

Links | BibTeX

Zou, Yujing

Fonds de recherche du Québec - Nature et technologies (FRQNT) PhD doctoral training scholarship Award

from Fonds de recherche du Québec in 2022.

Links | BibTeX

Zou, Yujing

Fonds de recherche du Québec - Santé (FRQS) PhD doctoral training scholarship Award

from Fonds de recherche du Québec in 2022.

Links | BibTeX

Zou, Yujing; Lecavalier-Barsoum, Magali; Pelmus, Manuela; Maleki, Farhad; Enger, Shirin A.

Predictive modeling of post radiation-therapy recurrence for gynecological cancer patients using clinical and histopathology imaging features Conference

Curietherapies 2022.

Abstract | Links | BibTeX

@conference{nokey,

title = {Predictive modeling of post radiation-therapy recurrence for gynecological cancer patients using clinical and histopathology imaging features},

author = {Yujing Zou and Magali Lecavalier-Barsoum and Manuela Pelmus and Farhad Maleki and Shirin A. Enger},

url = {https://www.researchgate.net/publication/361436138_Predictive_modeling_of_post_radiation-therapy_recurrence_for_gynecological_cancer_patients_using_clinical_and_histopathology_imaging_features},

year  = {2022},

date = {2022-05-23},

urldate = {2022-05-23},

organization = {Curietherapies},

abstract = {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 gynecological (i.e., cervix, vaginal, and vulva) cancer patients (median age at diagnosis = 59.5 years) with a median follow-up time of 25.7 months, nine of which (event rate of 25%) experienced post-RT recurrence, were included in this analysis. Patient-specific nuclei size and cell spacing distributions from cancerous and non-tumoral regions of pre-treatment hematoxylin and eosin (H&E) stained digital histopathology Whole-Slide-Images (WSI) were extracted. The mean and standard deviation of these distributions were computed as imaging features for each WSI. Clinical features of clinical and radiological stage at the time of radiation, p16 status, age at diagnosis, and cancer type were also obtained. Uniquely, a Tree-based Pipeline Optimization Tool (TPOT) AutoML approach, including hyperparameter tuning, was implemented to find the best performing pipeline for this class-imbalanced and small dataset. A Radial Basis Function Kernel (RBF) sampler (gamma = 0.25) was applied to combined imaging and clinical input variables for training. The resulting features were fed into an XGBoost (ie., eXtreme gradient-boosting) classifier (learning rate = 0.1). Its outputs were propagated as “synthetic features” followed by polynomial feature transforms. All raw and transformed features were trained with a decision tree classification algorithm. Results of model evaluation metrics from a 10-fold stratified shuffle split cross-validation were averaged. A permutation test (n=1000) was performed to validate the significance of the classification scores. Results: Our model achieved a 10-fold stratified shuffle split cross-validation scores of 0.87 for mean accuracy, 0.92 for mean balanced accuracy, 0.78 for precision, 1 for recall, 0.85 for F1 score, and 0.92 for Area Under the Curve of Receiver Operating Characteristics Curve, to predict our patient cohort’s post-RT recurrence binary outcome. A p-value of 0.036 was obtained from the permutation test. This implies real dependencies between our combined imaging and clinical features and outcomes which were learned by the classifier, and the primising model performance was not by chance. Conclusions: Despite the small dataset and low event rate, as a proof of concept, we showed that a decision-tree-based ML classification algorithm using an XGBoost algorithm is able to utilize combined (cell spacing & nuclei size) imaging and clinical features to predict post-RT outcomes for gynecological cancer patients.},

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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 gynecological (i.e., cervix, vaginal, and vulva) cancer patients (median age at diagnosis = 59.5 years) with a median follow-up time of 25.7 months, nine of which (event rate of 25%) experienced post-RT recurrence, were included in this analysis. Patient-specific nuclei size and cell spacing distributions from cancerous and non-tumoral regions of pre-treatment hematoxylin and eosin (H&E) stained digital histopathology Whole-Slide-Images (WSI) were extracted. The mean and standard deviation of these distributions were computed as imaging features for each WSI. Clinical features of clinical and radiological stage at the time of radiation, p16 status, age at diagnosis, and cancer type were also obtained. Uniquely, a Tree-based Pipeline Optimization Tool (TPOT) AutoML approach, including hyperparameter tuning, was implemented to find the best performing pipeline for this class-imbalanced and small dataset. A Radial Basis Function Kernel (RBF) sampler (gamma = 0.25) was applied to combined imaging and clinical input variables for training. The resulting features were fed into an XGBoost (ie., eXtreme gradient-boosting) classifier (learning rate = 0.1). Its outputs were propagated as “synthetic features” followed by polynomial feature transforms. All raw and transformed features were trained with a decision tree classification algorithm. Results of model evaluation metrics from a 10-fold stratified shuffle split cross-validation were averaged. A permutation test (n=1000) was performed to validate the significance of the classification scores. Results: Our model achieved a 10-fold stratified shuffle split cross-validation scores of 0.87 for mean accuracy, 0.92 for mean balanced accuracy, 0.78 for precision, 1 for recall, 0.85 for F1 score, and 0.92 for Area Under the Curve of Receiver Operating Characteristics Curve, to predict our patient cohort’s post-RT recurrence binary outcome. A p-value of 0.036 was obtained from the permutation test. This implies real dependencies between our combined imaging and clinical features and outcomes which were learned by the classifier, and the primising model performance was not by chance. Conclusions: Despite the small dataset and low event rate, as a proof of concept, we showed that a decision-tree-based ML classification algorithm using an XGBoost algorithm is able to utilize combined (cell spacing & nuclei size) imaging and clinical features to predict post-RT outcomes for gynecological cancer patients.

Zou, Yujing; Lecavalier-Barsoum, Magali; Pelmus, Manuela; Maleki, Farhad; Enger, Shirin A.

Young Investigator Competition Winner at the Curietherapies Conference Award

from Curietherapies in 2022.

Abstract | Links | BibTeX

@award{nokey,

title = {Young Investigator Competition Winner at the Curietherapies Conference },

author = {Yujing Zou and Magali Lecavalier-Barsoum and Manuela Pelmus and Farhad Maleki and Shirin A. Enger },

url = {https://www.researchgate.net/publication/360979157_SP-0014_McMedHacks_Deep_learning_for_medical_image_analysis_workshops_and_Hackathon_in_radiation_oncology},

year = {2022},

date = {2022-05-23},

urldate = {2022-05-23},

organization = {Curietherapies},

abstract = {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 participants’ area of expertise, country of origin, level of study, and level of programming skills. The weekly workshops were instructed by 8 students and experts assisted by 20 mentors who provided weekly tutorials. Recent developments in DL and medical physics were highlighted by 21 leaders from industry and academia. A virtual grand challenge Hackathon took place at the end of the workshop series. All events were held virtually and recorded on Zoom to accommodate all time zones and locations. The workshops were designed as interactive coding demos and shared through Google Colab notebooks. Results: McMedHacks gained 356 registrations from participants of 38 different countries (Fig. 1) from undergraduates, to PhDs and MDs. A vast number of disciplines and professions were represented, dominated by medical physics students, academic, and clinical medical physicists (Fig. 2). Sixty-nine participants earned a certificate of completion by having engaged with at least 12 of all 14 events. The program received participant feedback average scores of 4.768, 4.478, 4.579, 4.292, 4.84 out of five for the qualities of presentation, workshop session, tutorial and mentor, assignments, and course delivery, respectively. The eight-week long workshop’s duration allowed participants to digest the taught materials in a continuous manner as opposed to bootcamp-style conference workshops. Conclusion: The overwhelming interest and engagement for the McMedHacks workshop series from the Radiation Oncology (RadOnc) community illustrates a demand for Artificial Intelligence (AI) education in RadOnc. The future of RadOnc clinics will inevitably integrate AI. Therefore, current RadOnc professionals, and student and resident trainees should be prepared to understand basic AI principles and its applications to troubleshoot, innovate, and collaborate. McMedHacks set an excellent example of promoting open and multidisciplinary education, scientific communication, and leadership for integrating AI education into the RadOnc community on an international level. Therefore, we advocate for implementation of AI curriculums in professional education programs such as Commission on Accreditation of Medical Physics Education Programs (CAMPEP). Furthermore, we encourage experts from around the world in the field of AI, or RadOnc, or both, to take initiatives like McMedHacks to collaborate and push forward AI education in their departments and lead practical workshops, regardless of their levels of education.},

keywords = {},

pubstate = {published},

tppubtype = {award}

}

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 participants’ area of expertise, country of origin, level of study, and level of programming skills. The weekly workshops were instructed by 8 students and experts assisted by 20 mentors who provided weekly tutorials. Recent developments in DL and medical physics were highlighted by 21 leaders from industry and academia. A virtual grand challenge Hackathon took place at the end of the workshop series. All events were held virtually and recorded on Zoom to accommodate all time zones and locations. The workshops were designed as interactive coding demos and shared through Google Colab notebooks. Results: McMedHacks gained 356 registrations from participants of 38 different countries (Fig. 1) from undergraduates, to PhDs and MDs. A vast number of disciplines and professions were represented, dominated by medical physics students, academic, and clinical medical physicists (Fig. 2). Sixty-nine participants earned a certificate of completion by having engaged with at least 12 of all 14 events. The program received participant feedback average scores of 4.768, 4.478, 4.579, 4.292, 4.84 out of five for the qualities of presentation, workshop session, tutorial and mentor, assignments, and course delivery, respectively. The eight-week long workshop’s duration allowed participants to digest the taught materials in a continuous manner as opposed to bootcamp-style conference workshops. Conclusion: The overwhelming interest and engagement for the McMedHacks workshop series from the Radiation Oncology (RadOnc) community illustrates a demand for Artificial Intelligence (AI) education in RadOnc. The future of RadOnc clinics will inevitably integrate AI. Therefore, current RadOnc professionals, and student and resident trainees should be prepared to understand basic AI principles and its applications to troubleshoot, innovate, and collaborate. McMedHacks set an excellent example of promoting open and multidisciplinary education, scientific communication, and leadership for integrating AI education into the RadOnc community on an international level. Therefore, we advocate for implementation of AI curriculums in professional education programs such as Commission on Accreditation of Medical Physics Education Programs (CAMPEP). Furthermore, we encourage experts from around the world in the field of AI, or RadOnc, or both, to take initiatives like McMedHacks to collaborate and push forward AI education in their departments and lead practical workshops, regardless of their levels of education.

Zou, Yujing

Biological & Biomedical Engineering PhD Recruitment Award Award

from McGill Biological & Biomedical Engineering department in 2022.

Links | BibTeX

Zou, Yujing

Graduate Research Enhancement and Travel Awards (GREAT Awards) Award

from in 2022.

Links | BibTeX

Zou, Yujing; Lecavalier-barsoum, Magali; Pelmus, Manuela; Enger, Shirin A.

Patient-Specific Nuclei Size and Cell Spacing Distribution Extraction From Histopathology Whole Slide Images for Treatment Outcome Prediction Modelling Inproceedings

In: MEDICAL PHYSICS, pp. E266–E266, WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2022.

Abstract | Links | BibTeX

@inproceedings{zou2022patient,

title = {Patient-Specific Nuclei Size and Cell Spacing Distribution Extraction From Histopathology Whole Slide Images for Treatment Outcome Prediction Modelling},

author = {Yujing Zou and Magali Lecavalier-barsoum and Manuela Pelmus and Shirin A. Enger},

url = {https://w4.aapm.org/meetings/2022AM/programInfo/programAbs.php?sid=10686&aid=66642},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {MEDICAL PHYSICS},

volume = {49},

number = {6},

pages = {E266--E266},

organization = {WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA},

abstract = {MO-C930-IePD-F5-1 (Monday, 7/11/2022) 9:30 AM - 10:00 AM [Eastern Time (GMT-4)]



Exhibit Hall | Forum 5



Purpose: To deliver a fully automated and generalizable approach extracting patient-specific nuclei size (ns) and cell spacing (cs) distributions from cancerous and non-tumoral regions of hematoxylin and eosin (H&E) stained digital histopathology Whole-Slide-Images (WSI) for gynecological cancer multiscale treatment outcome modelling.



Methods: Each pre-treatment gigapixel H&E WSI digitized at 40 x magnification (0.2482 microns/pixel) were divided into 5000 x 5000-pixel patches. Within each patch, the nucleus centers were identified by a difference of gaussians blob detection algorithm obtaining Delaunay triangulations and Voronoi diagrams providing cs radius. The ns radius was computed from stained pixels dominated by hematoxylin content with an automatic thresholding algorithm. With multiprocessing CPUs on a PC for each WSI, eight feature types were calculated preserving biopsies tissue heterogeneity: the mean and standard deviation of cs and ns distributions concatenated from all patches for cancerous and non-tumoral regions. This method was applied to 40 patients (1 WSI per patient) with treatment outcomes of post radiation-therapy (RT) recurrence (n = 9),and death (n = 8)).



Results: The WSI cancerous region cs distribution mean among patients without post-RT recurrence has a median of 6.64 microns, and those with post-RT recurrence with a median of 7.11 microns. This indicates the potential of utilizing such distribution features in treatment outcome prognosis modelling. Furthermore, at the third quartile, the WSI non-tumoral region ns distribution standard deviation among patients without post-RT recurrence has a value of 2.16 microns, and 1.46 microns for those with post-RT recurrence.



Conclusion: Our approach derives patient-specific microscopic data distributions from histopathology WSI that can be directly associated with retrospective patient outcomes. They are complementary and spatially orthogonal to information served by other medical imaging modalities such as CT, MR, and Ultrasound. Therefore, it has the unique potential to augment treatment outcome model inference when properly fused with radiological scans.



Funding Support, Disclosures, and Conflict of Interest: CIHR grant number 103548 and Canada Research Chairs Program (grant #252135)



Keywords



Image Analysis, Feature Extraction, Radiation Therapy



Taxonomy



IM/TH- Image Analysis (Single Modality or Multi-Modality): Imaging biomarkers and radiomics



Contact Email



yujing.zou@mail.mcgill.ca},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

MO-C930-IePD-F5-1 (Monday, 7/11/2022) 9:30 AM - 10:00 AM [Eastern Time (GMT-4)]

Exhibit Hall | Forum 5

Purpose: To deliver a fully automated and generalizable approach extracting patient-specific nuclei size (ns) and cell spacing (cs) distributions from cancerous and non-tumoral regions of hematoxylin and eosin (H&E) stained digital histopathology Whole-Slide-Images (WSI) for gynecological cancer multiscale treatment outcome modelling.

Methods: Each pre-treatment gigapixel H&E WSI digitized at 40 x magnification (0.2482 microns/pixel) were divided into 5000 x 5000-pixel patches. Within each patch, the nucleus centers were identified by a difference of gaussians blob detection algorithm obtaining Delaunay triangulations and Voronoi diagrams providing cs radius. The ns radius was computed from stained pixels dominated by hematoxylin content with an automatic thresholding algorithm. With multiprocessing CPUs on a PC for each WSI, eight feature types were calculated preserving biopsies tissue heterogeneity: the mean and standard deviation of cs and ns distributions concatenated from all patches for cancerous and non-tumoral regions. This method was applied to 40 patients (1 WSI per patient) with treatment outcomes of post radiation-therapy (RT) recurrence (n = 9),and death (n = 8)).

Results: The WSI cancerous region cs distribution mean among patients without post-RT recurrence has a median of 6.64 microns, and those with post-RT recurrence with a median of 7.11 microns. This indicates the potential of utilizing such distribution features in treatment outcome prognosis modelling. Furthermore, at the third quartile, the WSI non-tumoral region ns distribution standard deviation among patients without post-RT recurrence has a value of 2.16 microns, and 1.46 microns for those with post-RT recurrence.

Conclusion: Our approach derives patient-specific microscopic data distributions from histopathology WSI that can be directly associated with retrospective patient outcomes. They are complementary and spatially orthogonal to information served by other medical imaging modalities such as CT, MR, and Ultrasound. Therefore, it has the unique potential to augment treatment outcome model inference when properly fused with radiological scans.

Funding Support, Disclosures, and Conflict of Interest: CIHR grant number 103548 and Canada Research Chairs Program (grant #252135)

Keywords

Image Analysis, Feature Extraction, Radiation Therapy

Taxonomy

IM/TH- Image Analysis (Single Modality or Multi-Modality): Imaging biomarkers and radiomics

Contact Email

yujing.zou@mail.mcgill.ca

Zou, Yujing; Weishaupt, Luca; Enger, Shirin A.

SP-0014 McMedHacks: Deep learning for medical image analysis workshops and Hackathon in radiation oncology Journal Article

In: Radiotherapy and Oncology, 170 , pp. S4–S5, 2022.

Abstract | Links | BibTeX

@article{zou2022sp,

title = {SP-0014 McMedHacks: Deep learning for medical image analysis workshops and Hackathon in radiation oncology},

author = {Yujing Zou and Luca Weishaupt and Shirin A. Enger},

url = {https://www-sciencedirect-com.proxy3.library.mcgill.ca/science/article/pii/S0167814022038695?via%3Dihub},

doi = {10.1016/S0167-8140(22)03869-5},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Radiotherapy and Oncology},

volume = {170},

pages = {S4--S5},

publisher = {Elsevier},

abstract = {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 participants’ area of expertise, country of origin, level of study, and level of programming skills. The weekly workshops were instructed by 8 students and experts assisted by 20 mentors who provided weekly tutorials. Recent developments in DL and medical physics were highlighted by 21 leaders from industry and academia. A virtual grand challenge Hackathon took place at the end of the workshop series. All events were held virtually and recorded on Zoom to accommodate all time zones and locations. The workshops were designed as interactive coding demos and shared through Google Colab notebooks. Results: McMedHacks gained 356 registrations from participants of 38 different countries (Fig. 1) from undergraduates, to PhDs and MDs. A vast number of disciplines and professions were represented, dominated by medical physics students, academic, and clinical medical physicists (Fig. 2). Sixty-nine participants earned a certificate of completion by having engaged with at least 12 of all 14 events. The program received participant feedback average scores of 4.768, 4.478, 4.579, 4.292, 4.84 out of five for the qualities of presentation, workshop session, tutorial and mentor, assignments, and course delivery, respectively. The eight-week long workshop’s duration allowed participants to digest the taught materials in a continuous manner as opposed to bootcamp-style conference workshops. Conclusion: The overwhelming interest and engagement for the McMedHacks workshop series from the Radiation Oncology (RadOnc) community illustrates a demand for Artificial Intelligence (AI) education in RadOnc. The future of RadOnc clinics will inevitably integrate AI. Therefore, current RadOnc professionals, and student and resident trainees should be prepared to understand basic AI principles and its applications to troubleshoot, innovate, and collaborate. McMedHacks set an excellent example of promoting open and multidisciplinary education, scientific communication, and leadership for integrating AI education into the RadOnc community on an international level. Therefore, we advocate for implementation of AI curriculums in professional education programs such as Commission on Accreditation of Medical Physics Education Programs (CAMPEP). Furthermore, we encourage experts from around the world in the field of AI, or RadOnc, or both, to take initiatives like McMedHacks to collaborate and push forward AI education in their departments and lead practical workshops, regardless of their levels of education.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

2021

Zou, Yujing; Lecavalier-Barsoum, Magali; Enger, Shirin A.

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.

Abstract | BibTeX

2020

Zou, Yujing

Graduate Excellence Fellowship Award

from McGill Medical Physics Unit in 2020.

BibTeX

Presentations

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 7^th,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 3^rd, 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 1^st, 2021, 17 min Institutional Oral Presentation. [schedule]
Y.Zou†*, L.L.Weishaupt*, P.Watson*, “Ultrasound and instance segmentation”, July 10^th, 2021, McMedHacks Workshop Week 5, 1.5-hour lecture. [link]
Y.Zou†*, L.L.Weishaupt*, “Digital histopathological image analysis”, June 25^th, 2021, McMedHacks Workshop Week 3, 1.5-hour lecture. [link]
Y.Zou†*, L.L.Weishaupt*, “Introduction to Python for deep learning”, June 12^th, 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.