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Generalization Properties of Geometric 3D Deep Learning Models for Medical Segmentation., , , , , , , , , and . ISBI, page 1-5. IEEE, (2023)Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies., , and . NeuroImage, (2019)Expectation-Maximization with Image-Weighted Markov Random Fields to Handle Severe Pathology., , , , , and . DICTA, page 1-6. IEEE, (2015)MRI Denoising and Artefact Removal Using Self-Organizing Maps for Fast Global Block-Matching., , , , and . Patch-MI@MICCAI, volume 11075 of Lecture Notes in Computer Science, page 20-27. Springer, (2018)Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model., , , , , , , , , and . NeuroImage, (2020)Early brain morphometrics from neonatal MRI predict motor and cognitive outcomes at 2-years corrected age in very preterm infants., , , , , , , , , and 5 other author(s). NeuroImage, (2023)Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-organizing Maps., and . DLMIA/ML-CDS@MICCAI, volume 11045 of Lecture Notes in Computer Science, page 110-118. Springer, (2018)Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach., , , , , , , and . DATRA/PIPPI@MICCAI, volume 11076 of Lecture Notes in Computer Science, page 87-96. Springer, (2018)Automated injury segmentation to assist in the treatment of children with cerebral palsy. University of Queensland, Australia, (2017)