Ravi HASSANALY

12
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

Image document

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 (California), United States. ⟨10.1117/12.2691683⟩
Communication dans un congrès hal-04291561 v2
Image document

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-05444841 v1
Image document

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-04419141 v1
Image document

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-04185304 v1
Image document

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-03835015 v2
Image document

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-04279014 v1
Image document

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
Poster de conférence hal-05443392 v1
Image document

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-05443417 v1