Geoffroy Oudoumanessah
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View article: Cluster Globally, Reduce Locally: Scalable Efficient Dictionary Compression for Magnetic Resonance Fingerprinting
Cluster Globally, Reduce Locally: Scalable Efficient Dictionary Compression for Magnetic Resonance Fingerprinting Open
With the rapid advancements in medical data acquisition and production, increasingly richer representations exist to characterize medical information. However, such large-scale data do not usually meet computing resource constraints or alg…
View article: Scalable magnetic resonance fingerprinting: Incremental inference of\n high dimensional elliptical mixtures from large data volumes
Scalable magnetic resonance fingerprinting: Incremental inference of\n high dimensional elliptical mixtures from large data volumes Open
Magnetic Resonance Fingerprinting (MRF) is an emerging technology with the\npotential to revolutionize radiology and medical diagnostics. In comparison to\ntraditional magnetic resonance imaging (MRI), MRF enables the rapid,\nsimultaneous,…
View article: Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings Open
Bayesian inference for complex models with an intractable likelihood can be tackled using algorithms performing many calls to computer simulators. These approaches are collectively known as "simulation-based inference" (SBI). Recent SBI me…
View article: Towards frugal unsupervised detection of subtle abnormalities in medical imaging
Towards frugal unsupervised detection of subtle abnormalities in medical imaging Open
Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. This problem can be addressed through unsupervised anomaly detection (UAD) methods, which identify features that do not match wit…
View article: Brain Subtle Anomaly Detection Based on Auto-Encoders Latent Space Analysis: Application To De Novo Parkinson Patients
Brain Subtle Anomaly Detection Based on Auto-Encoders Latent Space Analysis: Application To De Novo Parkinson Patients Open
Neural network-based anomaly detection remains challenging in clinical\napplications with little or no supervised information and subtle anomalies such\nas hardly visible brain lesions. Among unsupervised methods, patch-based\nauto-encoder…
View article: Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients
Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients Open
Neural network-based anomaly detection remains challenging in clinical applications with little or no supervised information and subtle anomalies such as hardly visible brain lesions. Among unsupervised methods, patch-based auto-encoders w…