Ravi HASSANALY

Postdoctoral researcher
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
Contact

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 cerveau

Maëlys Solal , Ravi Hassanaly , Ninon Burgos

IABM 2025 - Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, Mar 2025, Nice, France

Poster de conférence hal-05444797v1
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Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET

Maëlys Solal , Ravi Hassanaly , Ninon Burgos

CURE-ND (Catalyzing a United Response in Europe to Neurodegenerative Diseases) Early Career Researchers Workshop, Mar 2024, Bonn, Germany

Poster de conférence hal-05443417v1
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Exploitation de la variabilité de la population contrôle pour la détection d'anomalies non supervisée par apprentissage profond en imagerie cérébrale FDG PET

Maëlys Solal , Ravi Hassanaly , Ninon Burgos

IABM 2024 - Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, Mar 2024, Grenoble, France

Communication dans un congrès hal-05443392v1
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Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET

Maëlys Solal , Ravi Hassanaly , Ninon Burgos

SPIE Medical Imaging, Feb 2024, San Diego, United States. ⟨10.1117/12.2691683⟩

Communication dans un congrès hal-04291561v2
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Evaluation of pseudo-healthy reconstruction for anomaly detection in brain FDG PET

Ravi Hassanaly , Camille Brianceau , Maëlys Solal , Olivier Colliot , Ninon Burgos

MIDL 2024 - Medical Imaging with Deep Learning, Jul 2024, Paris, France

Communication dans un congrès hal-05444841v1
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Recent advances in the open-source ClinicaDL software for reproducible neuroimaging with deep learning

Ravi Hassanaly , Camille Brianceau , Mauricio Diaz , Sophie Loizillon , Elina Thibeau-Sutre et al.

SPIE Medical Imaging, Feb 2024, San Diego, United States. pp.519-524, ⟨10.1117/12.3006039⟩

Communication dans un congrès hal-04419141v1
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Unsupervised anomaly detection in 3D brain FDG PET: A benchmark of 17 VAE-based approaches

Ravi Hassanaly , Camille Brianceau , Olivier Colliot , Ninon Burgos

Deep Generative Models workshop at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Oct 2023, Vancouver, Canada

Communication dans un congrès hal-04185304v1
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Simulation-based evaluation framework for deep learning unsupervised anomaly detection on brain FDG PET

Ravi Hassanaly , Simona Bottani , Benoît Sauty , Olivier Colliot , Ninon Burgos

SPIE Medical Imaging, Feb 2023, San Diego, United States

Communication dans un congrès hal-03835015v2
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ClinicaDL: an open-source deep learning software for reproducible neuroimaging processing

Elina Thibeau-Sutre , Mauricio Diaz , Ravi Hassanaly , Olivier Colliot , Ninon Burgos

OHBM 2022 - Annual meeting of the Organization for Human Brain Mapping, Jun 2022, Glasgow, United Kingdom

Communication dans un congrès hal-04279014v1