13 citations · 36 across the 17 of their papers we have counts for
4 papers · 1 filter
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…
Nodal statistics-based equivalence relation for graph collections
Lucrezia Carboni, Michel Dojat, Sophie Achard
Node role explainability in complex networks is very difficult, yet is crucial in different application domains such as social science, neurosciences or computer science. Many effo…
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…