13 citations · 16 across the 7 of their papers we have counts for
3 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…
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…