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Ninon Burgos

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  • Home
  • Research projects
    • Ongoing research projects
      • Deep generative models for the detection of anomalies in brain PET images
      • Machine learning to exploit neuroimages in clinical data warehouses
    • Past research projects
      • PhD – Atlas-based methods for image synthesis
      • Postdoc – Joint segmentation and image synthesis
      • Postdoc – Individual analysis of PET images for the diagnosis of dementia
  • Publications
  • Software
  • ARAMIS Lab

Author: Ninon

13/11/2017 Uncategorized

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Latest publications
  • Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detection
  • Étude de la qualité des IRM T1 et FLAIR de routine clinique au sein d'un entrepôt de données de santé de l'AP-HP
  • Comparing Longitudinal Preprocessing Pipelines for Brain Volume Consistency in T1-Weighted MRI Test-Retest Scans
  • Assessing the Efficacy of Artefact Synthesis and Transfer Learning for Quality Control in Clinical 3D FLAIR Brain MRI
  • Unsupervised anomaly detection in brain FDG PET with deep generative models: An experimental analysis of model variability and mitigation strategies
  • Artificial intelligence in presymptomatic neurological diseases: Bridging normal variation and prodromal signatures
  • Benchmarking 3D generative autoencoders for pseudo-healthy reconstruction of brain 18F-fluorodeoxyglucose positron emission tomography
  • Unsupervised anomaly detection using Bayesian flow networks: application to brain FDG pet in the context of Alzheimer’s disease
  • Automatic quality control of brain 3D FLAIR MRIs for a clinical data warehouse
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