Ravi Hassanaly

Aramis Lab. Paris Brain Institute. rhassana96@gmail.com

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I am a postdoctoral researcher at the Paris Brain Institute, currently working in the ARAMIS Lab under the supervision of Ninon Burgos and Olivier Colliot. My research focuses on developing and applying deep learning generative methods for medical imaging, with a specific interest in diffusion models for anomaly detection in neuroimaging.

I combine methodological innovation in generative models and anomaly detection techniques with a strong focus on clinical applications, aiming to improve diagnostic tools and patient outcomes in the case neurodegenerative disease. I’m also an active contributor to open-source projects, further advancing the field of medical AI.

I earned my PhD in computer science from Sorbonne University in April 2024, and I hold an engineering degree from Ecole Centrale de Lyon and a master in data science from Université de Lyon 1.

When I’m not working on AI or medical imaging, you’ll often find me playing soccer or riding my gravel bike!

news

Sep 1, 2024 Really excited to start (officially) my postdoc à ARAMIS Lab.
Jun 24, 2024 Hello from Eindhoven ! #GemSS2024
Apr 30, 2024 Succesfully defended my PhD ! I am a doctor now :)
Feb 18, 2024 Hello from San Diego ! #SPIEMedicalImaging2024
Jan 1, 2024 Paper accepted in MELBA special issue for generative models !
Dec 1, 2023 Paper accepted for SPIE Medical Imaging 2024 in San Diego.
Oct 9, 2023 Hello from Vancouver ! #MICCAI2023

selected publications

  1. MELBA
    Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to Brain FDG PET
    Ravi Hassanaly, Camille Brianceau, Maëlys Solal, and 2 more authors
    Machine Learning for Biomedical Imaging, 2024
  2. DGM4MICCAI
    Unsupervised anomaly detection in 3D brain FDG PET: A benchmark of 17 VAE-based approaches
    Ravi Hassanaly, Camille Brianceau, Olivier Colliot, and 1 more author
    In Deep Generative Models workshop at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), 2023
  3. ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing
    Elina Thibeau-Sutre, Mauricio Diaz, Ravi Hassanaly, and 4 more authors
    Computer Methods and Programs in Biomedicine, 2022