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While making a tremendous impact in various fields, deep neural networks usually require large amounts of labeled data for training which are expensive to collect in many applications, especially in the medical domain.
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User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
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Fully convolutional networks for semantic segmentation
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Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation
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Uncertainty in deep learning
Y. Gal · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Temporal ensembling for semi-supervised learning
S. Laine and T. Aila · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
F. Milletari, N. Navab, and S.-A. Ahmadi · 2016
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Improving computer-aided detection using convolutional neural networks and random view aggregation
H. R. Roth, L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim, and R. M. Summers · 2016
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A fixed-point model for pancreas segmentation in abdominal ct scans
Y. Zhou, L. Xie, W. Shen, Y. Wang, E. K. Fishman, and A. L. Yuille · 2017
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Tri-net for semi-supervised deep learning
D.-D. Chen, W. Wang, W. Gao, and Z.-H. Zhou · 2018
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
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V. Cheplygina, M. de Bruijne, and J. P. Pluim · 2018
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Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
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Semi-supervised learning for network-based cardiac mr image segmentation
W. Bai, O. Oktay, M. Sinclair, H. Suzuki, M. Rajchl, G. Tarroni, B. Glocker, A. King, P. M. Matthews, and D. Rueckert · 2017
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Quo vadis, action recognition? a new model and the kinetics dataset
J. Carreira and A. Zisserman · 2017
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Rethinking atrous convolution for semantic image segmentation
L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
A. Kendall and Y. Gal · 2017
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Tumor-aware, adversarial domain adaptation from ct to mri for lung cancer segmentation
J. Jiang, Y.-C. Hu, N. Tyagi, P. Zhang, A. Rimner, G. S. Mageras, J. O. Deasy, and H. Veeraraghavan · 2018
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Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model
X. Li, L. Yu, H. Chen, C.-W. Fu, and P.-A. Heng · 2018
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3d anisotropic hybrid network: Transferring convolutional features from 2d images to 3d anisotropic volumes
S. Liu, D. Xu, S. K. Zhou, O. Pauly, S. Grbic, T. Mertelmeier, J. Wicklein, A. Jerebko, W. Cai, and D. Comaniciu · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
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Asdnet: Attention based semi-supervised deep networks for medical image segmentation
D. Nie, Y. Gao, L. Wang, and D. Shen · 2018
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Deep co-training for semi-supervised image recognition
S. Qiao, W. Shen, Z. Zhang, B. Wang, and A. Yuille · 2018
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Bridging the gap between 2d and 3d organ segmentation with volumetric fusion net
Y. Xia, L. Xie, F. Liu, Z. Zhu, E. K. Fishman, and A. L. Yuille · 2018
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Transformation consistent self-ensembling model for semi-supervised medical image segmentation
X. Li, L. Yu, H. Chen, C.-W. Fu, and P.-A. Heng · 2019
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Semi-supervised multi-organ segmentation via multi-planar co-training
Y. Zhou, Y. Wang, P. Tang, W. Shen, E. K. Fishman, and A. L. Yuille · 2019
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