Ravi HASSANALY
- Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris] (ARAMIS)
Présentation
I am a postdoctoral researcher at the Paris Brain Institute, specializing in the application of deep generative models, particularly score-based generative models, for unsupervised anomaly detection in brain imaging. My PhD at the Paris Brain Institute and Sorbonne University provided me with a strong foundation in AI and medical imaging, along with advanced programming skills in Python, deep learning frameworks, and high-performance computing.
Domaines de recherche
Publications
Publications
|
|
Étude de la variabilité des modèles et construction de cartes d'anomalies tenant compte de l'incertitude à l'aide de modèles génératifs profonds non supervisés dans le cadre de la TEP-FDG en 3D du cerveauIABM 2025 - Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, Mar 2025, Nice, France |
|
|
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PETCURE-ND (Catalyzing a United Response in Europe to Neurodegenerative Diseases) Early Career Researchers Workshop, Mar 2024, Bonn, Germany |
|
|
Artificial intelligence in presymptomatic neurological diseases: Bridging normal variation and prodromal signaturesRevue Neurologique, 2025, 181 (9), pp.944-950. ⟨10.1016/j.neurol.2025.07.011⟩ |
|
|
Benchmarking 3D generative autoencoders for pseudo-healthy reconstruction of brain 18F-fluorodeoxyglucose positron emission tomographyJournal of Medical Imaging, 2025, 12 (5), pp.054005. ⟨10.1117/1.JMI.12.5.054005⟩ |
|
|
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETJournal of Machine Learning for Biomedical Imaging, 2024, Special Issue for Generative Models, 2, pp.611. ⟨10.59275/j.melba.2024-b87a⟩ |
|
|
Exploitation de la variabilité de la population contrôle pour la détection d'anomalies non supervisée par apprentissage profond en imagerie cérébrale FDG PETIABM 2024 - Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, Mar 2024, Grenoble, France |
|
|
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PETSPIE Medical Imaging, Feb 2024, San Diego, United States. ⟨10.1117/12.2691683⟩ |
|
|
Evaluation of pseudo-healthy reconstruction for anomaly detection in brain FDG PETMIDL 2024 - Medical Imaging with Deep Learning, Jul 2024, Paris, France |
|
|
Recent advances in the open-source ClinicaDL software for reproducible neuroimaging with deep learningSPIE Medical Imaging, Feb 2024, San Diego, United States. pp.519-524, ⟨10.1117/12.3006039⟩ |
|
|
Unsupervised anomaly detection in 3D brain FDG PET: A benchmark of 17 VAE-based approachesDeep Generative Models workshop at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Oct 2023, Vancouver, Canada |
|
|
Simulation-based evaluation framework for deep learning unsupervised anomaly detection on brain FDG PETSPIE Medical Imaging, Feb 2023, San Diego, United States |
|
|
ClinicaDL: an open-source deep learning software for reproducible neuroimaging processingOHBM 2022 - Annual meeting of the Organization for Human Brain Mapping, Jun 2022, Glasgow, United Kingdom |
|
|
Pseudo-healthy image reconstruction with deep generative models for the detection of dementia-related anomaliesMedical Imaging. Sorbonne Université, 2024. English. ⟨NNT : 2024SORUS118⟩ |