• Philippe Després

    Professor
    Faculté des sciences et de génie
    Université Laval

    Philippe Després is a full professor in the Department of Physics, Engineering Physics and Optics and member of the Cancer Research Center of Université Laval, medical physicist at CHU de Québec and a regular researcher at its affiliated Research Center, membrer of the Institute intelligence and data of Université Laval and member of the Researcher Council of the New Digital Research Infrastructure Organization (NDRIO). He is the designated principal investigator of the RHHDS program.

    He was trained at Université Laval (MSc 2000, Physics), Université de Montréal (PhD 2005, Physics) and University of California, San Francisco (postdoc 2005-2007, Biomedical Engineering, Molecular Imaging). 

    Professor Philippe Després is involved in several projects encompassing hardware and software aspects of medical imaging modalities, notably low-dose X-ray imaging, advanced imaging techniques, and solid-state detectors for molecular imaging. He pioneered high-performance computing (HPC) approaches with commodity graphics hardware (GPUs) that led to innovative applications in image processing/reconstruction and radiation dose calculations, including a fast GPU-based Monte Carlo engine to simulate energy transport in matter (GPUMCD).

    As a HPC expert, professor Philippe Després is also involved in data-driven research approaches, data infrastructures and FAIR-compliant research data management. In this regard, he is responsible of biomedical data at CHU de Québec of Université Laval Research Center, the data architect of the PULSAR health research platform, the assistant director of the Big Data Research Center at Université Laval, and the co-lead of the sustainable health axis of the Observatoire international sur les impacts sociétaux de l’intelligence artificielle et du numérique (OBVIA). 

  • Title of the research project
    Effect of oxygen pressure in cancerous tissue cells on radiotherapy treatments
    Description

    Radiotherapy treatments currently used in the clinical field are rarely modified. They generally consist of a global therapy of 50 grays, fractionated in five treatments of two grays every week for five weeks.
    Thus, it could be worthwhile to develop a numeric tool, based on mathematical models found in the literature, in order to compare different types of treatment without having to test them on real tissues. Several parameters are known to alter the tissue response after irradiation including oxygen

    Title of the research project
    Development of deep learning algorithms for clinical diagnosis using mass spectrometry data
    Description

    The first objective of the project is to design efficient convolutional network classification models (CNNs) using mass spectrometry data (1D and 2D) for clinical diagnosis (cancer and infection).

    Once finalized, the second objective is the interpretation of these classification models in order to identify spectral regions of interest that may correspond to new diagnosis or therapeutic biomarkers.

  • Oumaima Ouffy

    M.Sc. candidate
    Faculté des sciences et de génie
    Université Laval

    Student
    Directeur.e(s) de recherche
    Anne-Sophie Charest
    François Laviolette
    Start date
    Title of the research project
    Création d'un jeu de données synthétique pour des données de santé
    Description

    Il est souvent difficile de partager des données dénominalisées entre différentes organisations et chercheurs en raison de contraintes éthiques liées à la confidentialité des répondants. Il peut ainsi s’écouler de longs mois, parfois même des années, entre la rédaction d’un projet de recherche et le début de l’analyse planifiée, ce qui limite la capacité des chercheurs à mener des travaux scientifiques de pointe au moment opportun et contribue à allonger inutilement la formation d’étudiants gradués, entre autres problèmes. Une solution possible est de créer un jeu de données synthétiques à partager aux chercheurs en attente de l’accès au jeu de données original. Ce jeu de données synthétique serait représentatif des données originales, mais créé de façon à ne pas révéler d’information confidentielle sur les répondants. Il permettrait aux chercheurs de se familiariser à l’avance avec les variables mesurées, d’anticiper les difficultés techniques du projet de recherche (stockage, logiciels, gestion des accès), et de planifier de meilleurs protocoles de recherche.

    Nous étudions ici les enjeux techniques liés à la création de tels jeux de données synthétiques dans le domaine de la santé. Il faut notamment s’assurer que les modèles statistiques utilisés soient assez flexibles pour bien modéliser les corrélations entre les variables collectées, tout en s’assurant de ne pas sur-ajuster ceux-ci, ce qui pourrait nuire à la protection de la confidentialité. Le travail s’articulera autour de la création d’un jeu synthétique pour un sous-ensemble des données collectées par le Consortium d’identification précoce de la maladie d’Alzheimer - Québec (CIMA-Q), pour qui le partage des données à la communauté de recherche sur la maladie d’Alzheimer canadienne et internationale est un objectif important.
     

  • Khawla Seddiki

    Ph.D. candidate
    Faculté de médecine
    Université Laval

    Directeur.e(s) de recherche
    Arnaud Droit
    Start date
    Title of the research project
    Development of deep learning algorithms for clinical diagnosis using mass spectrometry data
    Description

    The first objective of the project is to design efficient convolutional network classification models (CNNs) using mass spectrometry data (1D and 2D) for clinical diagnosis (cancer and infection).

    Once finalized, the second objective is the interpretation of these classification models in order to identify spectral regions of interest that may correspond to new diagnosis or therapeutic biomarkers.

  • Corinne Chouinard

    Undergraduate intern
    Faculté des sciences et de génie
    Université Laval

    Directeur.e(s) de recherche
    Louis Archambault
    Michèle Desjardins
    Start date
    Title of the research project
    Effect of oxygen pressure in cancerous tissue cells on radiotherapy treatments
    Description

    Radiotherapy treatments currently used in the clinical field are rarely modified. They generally consist of a global therapy of 50 grays, fractionated in five treatments of two grays every week for five weeks.
    Thus, it could be worthwhile to develop a numeric tool, based on mathematical models found in the literature, in order to compare different types of treatment without having to test them on real tissues. Several parameters are known to alter the tissue response after irradiation including oxygen
    partial pressure in irradiated regions, particle type hitting the tissue, and treatment duration.

    The Python code created as the main part of the project is intended to facilitate the optimization of radiotherapy treatment by generating graphs showing cell survival after a certain number of fractions, taking many parameters into account. When completed and integrated to a graphical interface, the code will be easy to use and helpful for ongoing research projects.

  • Brandon Woolfson

    Undergraduate intern
    Medical Physics Unit
    McGill University

  • Discover

    Featured project

    Radiotherapy treatments currently used in the clinical field are rarely modified. They generally consist of a global therapy of 50 grays, fractionated in five treatments of two grays every week for five weeks.
    Thus, it could be worthwhile to develop a numeric tool, based on mathematical models found in the literature, in order to compare different types of treatment without having to test them on real tissues. Several parameters are known to alter the tissue response after irradiation including oxygen

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