13 citations · 23 across the 11 of their papers we have counts for
12 papers
Scalable magnetic resonance fingerprinting: Incremental inference of high dimensional elliptical mixtures from large data volumes
Geoffroy Oudoumanessah, Thomas Coudert, Carole Lartizien +3
Magnetic Resonance Fingerprinting (MRF) is an emerging technology with the potential to revolutionize radiology and medical diagnostics. In comparison to traditional magnetic reson…
Cluster globally, Reduce locally: Scalable efficient dictionary compression for magnetic resonance fingerprinting
Geoffroy Oudoumanessah, Thomas Coudert, Luc Meyer +4
With the rapid advancements in medical data acquisition and production, increasingly richer representations exist to characterize medical information. However, such large-scale dat…
Robust Conformal Volume Estimation in 3D Medical Images
Benjamin Lambert, Florence Forbes, Senan Doyle +1
Volumetry is one of the principal downstream applications of 3D medical image segmentation, for example, to detect abnormal tissue growth or for surgery planning. Conformal Predict…
Towards frugal unsupervised detection of subtle abnormalities in medical imaging
Geoffroy Oudoumanessah, Carole Lartizien, Michel Dojat +1
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 (…
Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification
Benjamin Lambert, Pauline Roca, Florence Forbes +2
The burden of liver tumors is important, ranking as the fourth leading cause of cancer mortality. In case of hepatocellular carcinoma (HCC), the delineation of liver and tumor on c…
Multi-layer Aggregation as a key to feature-based OOD detection
Benjamin Lambert, Florence Forbes, Senan Doyle +1
Deep Learning models are easily disturbed by variations in the input images that were not observed during the training stage, resulting in unpredictable predictions. Detecting such…
TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images
Benjamin Lambert, Florence Forbes, Senan Doyle +1
The volume of a brain lesion (e.g. infarct or tumor) is a powerful indicator of patient prognosis and can be used to guide the therapeutic strategy. Lesional volume estimation is u…
Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients
Nicolas Pinon, Geoffroy Oudoumanessah, Robin Trombetta +3
Neural network-based anomaly detection remains challenging in clinical applications with little or no supervised information and subtle anomalies such as hardly visible brain lesio…
Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation
Benjamin Lambert, Florence Forbes, Senan Doyle +2
Deep Learning models are easily disturbed by variations in the input images that were not seen during training, resulting in unpredictable behaviours. Such Out-of-Distribution (OOD…
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
Benjamin Lambert, Florence Forbes, Alan Tucholka +3
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particula…
Beyond Voxel Prediction Uncertainty: Identifying brain lesions you can trust
Benjamin Lambert, Florence Forbes, Senan Doyle +2
Deep neural networks have become the gold-standard approach for the automated segmentation of 3D medical images. Their full acceptance by clinicians remains however hampered by the…
Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients
Verónica Muñoz-Ramírez, Nicolas Pinon, Florence Forbes +2
Although neural networks have proven very successful in a number of medical image analysis applications, their use remains difficult when targeting subtle tasks such as the identif…