Ravi HASSANALY
13
Documents
Affiliation actuelle
- 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)
Identifiants chercheurs
Site web
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
Intelligence artificielle [cs.AI]
Imagerie médicale
Publications
Publications
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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
Poster de conférence
hal-05444797
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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
Poster de conférence
hal-05443417
v1
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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⟩
Article dans une revue
hal-05378338
v1
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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⟩
Article dans une revue
hal-04445378
v2
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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⟩
Article dans une revue
hal-04315738
v2
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Pseudo-healthy image reconstruction with deep generative models for the detection of dementia-related anomaliesMedical Imaging. Sorbonne Université, 2024. English. ⟨NNT : 2024SORUS118⟩
Thèse
tel-04681117
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