4 citations · 6 across the 3 of their papers we have counts for
3 papers
Latent-to-Latent Flow for Volumetric Stochastic Segmentation
Omar Todd, Sooha Kim, Raghav Mehta +5
Uncertainty arising from inter-observer variability in medical image segmentation plays an important role in developing treatment plans. Research in this area is inhibited by the l…
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…