3.6k citations · 3.6k across the 4 of their papers we have counts for
5 papers
Transductive image segmentation: Self-training and effect of uncertainty estimation
Konstantinos Kamnitsas, Stefan Winzeck, Evgenios N. Kornaropoulos +9
Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model…
Cranial Implant Design via Virtual Craniectomy with Shape Priors
Franco Matzkin, Virginia Newcombe, Ben Glocker +1
Cranial implant design is a challenging task, whose accuracy is crucial in the context of cranioplasty procedures. This task is usually performed manually by experts using computer…
Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy
Franco Matzkin, Virginia Newcombe, Susan Stevenson +6
Decompressive craniectomy (DC) is a common surgical procedure consisting of the removal of a portion of the skull that is performed after incidents such as stroke, traumatic brain…
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig +8
Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performan…
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
Konstantinos Kamnitsas, Christian Ledig, Virginia F. J. Newcombe +5
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the res…