2 papers
cs.LG2022
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation
Thomas Buddenkotte, Lorena Escudero Sanchez, Mireia Crispin-Ortuzar +6
Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only develop…
eess.IV2020
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg +7
This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients wit…