Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation. - Thomas Buddenkotte; Lorena Escudero Sanchez; Mireia Crispin-Ortuzar; Ramona Woitek; Cathal McCague; James D. Brenton; Ozan Öktem; Evis Sala; Leonardo Rundo (CoRR, 2022)

Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation. - Thomas Buddenkotte; Lorena Escudero Sanchez; Mireia Crispin-Ortuzar; Ramona Woitek; Cathal McCague; James D. Brenton; Ozan Öktem; Evis Sala; Leonardo Rundo (Comput. Biol. Medicine, 2023)

Automated Small Kidney Cancer Detection in Non-Contrast Computed Tomography. - William McGough; Thomas Buddenkotte; Stephan Ursprung; Zeyu Gao 001; Grant Stewart; Mireia Crispin-Ortuzar (ISBI, 2024)

Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning. - Roland Opfer; Tjalf Ziemssen; Julia Krüger; Thomas Buddenkotte; Lothar Spies; Carola Gocke; Matthias Schwab; Ralph Buchert (Comput. Biol. Medicine, 2024)

CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration. - Thomas Buddenkotte; Roland Opfer; Julia Krüger; Alessa Hering; Mireia Crispin-Ortuzar (CoRR, 2024)

BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI. - Roland Opfer; Julia Krüger; Thomas Buddenkotte; Lothar Spies; Finn Behrendt; Sven Schippling; Ralph Buchert (Int. J. Comput. Assist. Radiol. Surg., 2024)