5 citations · 13 across the 4 of their papers we have counts for
4 papers · 1 filter
Average Calibration Losses for Reliable Uncertainty in Medical Image Segmentation
Theodore Barfoot, Luis C. Garcia-Peraza-Herrera, Samet Akcay +2
Deep neural networks for medical image segmentation are often overconfident, compromising both reliability and clinical utility. In this work, we propose differentiable formulation…
Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
Meritxell Riera-Marin, Sikha O K, Julia Rodriguez-Comas +29
Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key…
Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation
Theodore Barfoot, Luis Garcia-Peraza-Herrera, Ben Glocker +1
Deep neural networks for medical image segmentation often produce overconfident results misaligned with empirical observations. Such miscalibration, challenges their clinical trans…
Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification
Talha Qaiser, Stefan Winzeck, Theodore Barfoot +9
Whole body magnetic resonance imaging (WB-MRI) is the recommended modality for diagnosis of multiple myeloma (MM). WB-MRI is used to detect sites of disease across the entire skele…