13 citations · 36 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
Graph-based methods coupled with specific distributional distances for adversarial attack detection
Dwight Nwaigwe, Lucrezia Carboni, Martial Mermillod +2
Artificial neural networks are prone to being fooled by carefully perturbed inputs which cause an egregious misclassification. These \textit{adversarial} attacks have been the focu…
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…