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QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy.

, , , , and . NeuroImage, (2019)

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Patient identification using high-confidence wavelet based Iris Pattern recognition., and . BHI, page 628-631. IEEE, (2012)Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling., , , and . MICCAI (1), volume 11070 of Lecture Notes in Computer Science, page 664-672. Springer, (2018)Complex Fully Convolutional Neural Networks for MR Image Reconstruction., , , , , and . MLMIR@MICCAI, volume 11074 of Lecture Notes in Computer Science, page 30-38. Springer, (2018)QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy., , , , and . NeuroImage, (2019)Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control., , , and . NeuroImage, (2019)Generalizability vs. Robustness: Adversarial Examples for Medical Imaging., , , and . CoRR, (2018)FatSegNet : A Fully Automated Deep Learning Pipeline for Adipose Tissue Segmentation on Abdominal Dixon MRI., , , , , , and . CoRR, (2019)CATARACTS: Challenge on automatic tool annotation for cataRACT surgery, , , , , , , , , and 28 other author(s). Medical Image Analysis, (2019)A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals., , and . Biomed. Signal Proc. and Control, 8 (6): 740-754 (2013)Metric hashing forests., , , , , , and . Medical Image Analysis, (2016)