Ravi HASSANALY Postdoctoral researcher

Identifiants chercheurs

IdHAL :

ravi-hassanaly

ORCID :

0009-0009-1304-5906

IdRef :

27996739X

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.

Publications

Article dans une revue (3)

Benchmarking 3D generative autoencoders for pseudo-healthy reconstruction of brain 18F-fluorodeoxyglucose positron emission tomography Ravi Hassanaly,Maëlys Solal,Olivier Colliot,Ninon Burgos Journal of Medical Imaging, 2025, 12 (5), pp.054005. ⟨10.1117/1.JMI.12.5.054005⟩ Article dans une revue hal-04445378v2
Artificial intelligence in presymptomatic neurological diseases: Bridging normal variation and prodromal signatures T. Soulier,Ninon Burgos,Ravi Hassanaly,M. Pitombeira,Maëlys Solalet al. Revue Neurologique, 2025, 181 (9), pp.944-950. ⟨10.1016/j.neurol.2025.07.011⟩ Article dans une revue hal-05378338v1
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET Ravi Hassanaly,Camille Brianceau,Maëlys Solal,Olivier Colliot,Ninon Burgos Journal 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-04315738v2

Communication dans un congrès (8)

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 Communication dans un congrès hal-05444797v1
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
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
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
Recent advances in the open-source ClinicaDL software for reproducible neuroimaging with deep learning Ravi Hassanaly,Camille Brianceau,Mauricio Diaz,Sophie Loizillon,Elina Thibeau-Sutreet al. SPIE Medical Imaging, Feb 2024, San Diego, United States. pp.519-524, ⟨10.1117/12.3006039⟩ Communication dans un congrès hal-04419141v1
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
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
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

Poster de conférence (1)

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

Thèse (1)

Pseudo-healthy image reconstruction with deep generative models for the detection of dementia-related anomalies Ravi Hassanaly Medical Imaging. Sorbonne Université, 2024. English. ⟨NNT : 2024SORUS118⟩ Thèse tel-04681117v1