• 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). 

  • Seyyedali Hosseini

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

  • Cédric Bélanger

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

  • Pierre-Luc Asselin

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

  • Title of the research project
    Development of automatic planning tools in high dose rate brachytherapy for prostate cancer
    Description

    Treatment of cancer with radiation is a proven technique used worldwide. One of the ways to treat prostate cancer is by using brachytherapy either alone or as a boost. At the moment, the techniques used depend on the experience of the treatment team and researchers are trying to overcome this problem.
    In our case, the technology considered to address this problem is deep learning. Therefore, the aim of this project is to use deep learning to develop tools for planning in high dose rate brachytherapy for the treatment of prostate cancer.

    Title of the research project
    Complex analyses with DataSHIELD for health data protection
    Description

    It is often difficult to share denominated data between different organisations and researchers due to ethical constraints related to respondent's confidentiality. This is a frequent reality in healthcare, given the inherent sensitivity of the data involved. One option in this case is to not share the data directly, but rather to provide access to it via a tool that controls the risk of disclosure of the queries made and allows only those it considers safe.

  • Niloofar Ziasaeedi

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

  • Discover

    Featured project

    The discovery of antimicrobial agents was one of the great triumphs of the 20th century. The sobering news is that antibiotic resistance was part of the process as well. If nothing is done by 2050, antimicrobial resistance  (AMR) will cost $100 trillion with 10M people/year expected to die (https://amr-review.org). Factors driving AMR extend beyond human healthcare with implications in veterinary medicine, agriculture and the environment (the One Health approach).

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