13 citations · 14 across the 3 of their papers we have counts for
4 papers
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…
Leveraging 3D Information in Unsupervised Brain MRI Segmentation
Benjamin Lambert, Maxime Louis, Senan Doyle +3
Automatic segmentation of brain abnormalities is challenging, as they vary considerably from one pathology to another. Current methods are supervised and require numerous annotated…