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Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control.

, , , and . NeuroImage, (2019)

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Supervised domain adaptation of decision forests: Transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization., , , , , , , and . Medical Image Analysis, (2016)Concurrent Spatial and Channel 'Squeeze & Excitation' in Fully Convolutional Networks., , and . MICCAI (1), volume 11070 of Lecture Notes in Computer Science, page 421-429. Springer, (2018)DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout., and . CoRR, (2016)Lumen Segmentation in Intravascular Optical Coherence Tomography Using Backscattering Tracked and Initialized Random Walks., , , , , , , , and . IEEE J. Biomedical and Health Informatics, 20 (2): 606-614 (2016)Bag of forests for modelling of tissue energy interaction in optical coherence tomography for atherosclerotic plaque susceptibility assessment., , , , , , , , , and . ISBI, page 428-431. IEEE, (2015)'Squeeze & excite' guided few-shot segmentation of volumetric images., , , , and . Medical Image Analysis, (2020)Importance Driven Continual Learning for Segmentation Across Domains., , , and . MLMI@MICCAI, volume 12436 of Lecture Notes in Computer Science, page 423-433. Springer, (2020)Hashing with Residual Networks for Image Retrieval., , , and . MICCAI (3), volume 10435 of Lecture Notes in Computer Science, page 541-549. Springer, (2017)Deep Residual Hashing., , , and . CoRR, (2016)Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks., , and . CoRR, (2018)