• Baptiste Bauvin

    Ph.D. candidate
    Faculté des sciences et de génie
    Université Laval

    Student
    Directeur.e(s) de recherche
    Jacques Corbeil
    Co-researcher
    Cécile Capponi
    Start date
    Title of the research project
    Multi-view supervised machine learning for solving multi-omics problems
    Description

    Supervised classification allows to build predictive models based on complex data to help human decision making processes. It has undergone an impressive development in recent years, particularly thanks to neural networks and the use of big data. However, these methods are not relevant to use on databases in which only a few instances are available to build the model, and even less when these instances are described by a large number of features. This type of problem, called fat data, is recurrent in the medical field, in which the extraction of data on patients is costly, but provides a large amount of information for each one. Moreover, in the medical field, it is common to perfrom several types of analysis on the same patient : genomic, metabolomic, transcriptomic, etc. This type of database is called multi-omics.

    The goal of this project is to use and develop multi-view classification algorithms relevant to the processing of multi-omic fat data

  • Christopher Bilodeau

    Undergraduate intern
    Faculté des sciences et de génie
    Université Laval

  • Alexandre Sagona

    Undergraduate intern
    Faculté des sciences et de génie
    Université Laval

  • Sophie Tran-Kiem

    Undergraduate intern
    Faculté des sciences et de génie
    Université Laval

  • Andréanne Allaire

    M.Sc. candidate

    Université de Sherbrooke

    Directeur.e(s) de recherche
    Martin Vallières
    Co-researcher
    Philippe Després
    Start date
    Title of the research project
    Systematic evaluation of robustness and exploitation potential of radiomic features in magnetic resonance imaging.
    Description

    In medical imaging, radiomic features make it possible to characterize heterogeneity of a region of interest at the anatomical level. This way of quantifying the heterogeneity of a region of interest can be useful, for example, in order to identify the more aggressive tumors in oncology. To do this, we hypothesize here that variation in magnetic resonance imaging (MRI) acquisition sequences and its resulting different levels of contrast would make it possible to optimize the subsequent radiomic analysis.
    In this project, a pipeline for the analysis of real medical images will first be set up in order to quantify the robustness of radiomic characteristics according to variations in acquisition protocols. Then, an MRI acquisition simulation pipeline will be developed in order to evaluate the potential for optimizing radiomic features in medicine.
     

  • Mahboubeh Motaghi

    Ph.D. candidate
    Faculté de médecine
    Université Laval

  • Title of the research project
    PARTAGE - Investigating patient-controlled data sharing for real-world evidence using the Opal patient portal
    Description

    The PARTAGE project (Patients and Researchers Team-up and Generate Evidence) is a research project that is examining mechanisms to allow patients to securely share their clinical data with researchers using the Opal patient portal. As part of the overall PARTAGE project, this specific sub-project is about obtaining stakeholder feedback on the concept of data-sharing.

    Title of the research project
    Dynamic dashboards for assessing the clinical relevance of medical imaging exams - Operational optimization
    Description

    The project consists in determining and exploring the possibilities offered by dynamic dashboards in a medical context as well as the associated data management structures. The project therefore considers several aspects of data management. In this sense, the considerations related to DICOM data transfers as well as different approaches to their management and conservation are considered. In addition, the dashboards will be designed to ensure an effective, clear and concise presentation with recognized visualization tools.

  • Brandon Woolfson

    Undergraduate intern
    Medical Physics Unit
    McGill University

    Directeur.e(s) de recherche
    John Kildea
    Start date
    Title of the research project
    PARTAGE - Investigating patient-controlled data sharing for real-world evidence using the Opal patient portal
    Description

    The PARTAGE project (Patients and Researchers Team-up and Generate Evidence) is a research project that is examining mechanisms to allow patients to securely share their clinical data with researchers using the Opal patient portal. As part of the overall PARTAGE project, this specific sub-project is about obtaining stakeholder feedback on the concept of data-sharing. To do so, we are using a process of stakeholder co-design in which patients, clinicians and researchers are embedded in the research team and we are obtaining additional feedback from each stakeholder group through focus groups and surveys.

  • Title of the research project
    An automated dose-to-organ estimator in diagnostic radiology
    Description

    In diagnostic radiology, the use of ionizing radiation is justified by benefits surpassing risks. From an epidemiological perspective, this balance is difficult to assess because accurate dose values for individuals are not available. This project consists in developing tools to automatically report dose-to-organs from Computed Tomography (CT) images. First, a machine-learning based, multiclass segmentation tool will be developed to automatically contour organs in CT imaging studies.

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    This research project is based on the analysis of massive data on the NOL index and other intraoperative clinical parameters used by anesthesiologists during surgery. These parameters help them make analgesic treatment decisions in a non-communicating patient under general anesthesia and in whom it is impossible to assess pain and analgesic needs by standard questionnaires performed on awake patients. 
    First, the objective is to interpret the values of this index in relation to the decisions made by the clinician. 

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