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Motivated by the problem of computer-aided detection (CAD) of pulmonary nodules, we introduce methods to propagate and fuse uncertainty information in a multi-stage Bayesian convolutional neural network (CNN) architecture.
The comparison and evaluation of forecasters
M. H. DeGroot and S. E. Fienberg · 1983
Earlier work this paper cites.
Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study
B. v. Ginneken et al · 2010
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U-Net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Variational dropout and the local reparameterization trick
D. P. Kingma, T. Salimans, and M. Welling · 2015
Earlier work this paper cites.
Recurrent convolutional networks for pulmonary nodule detection in CT imaging
P.-P. Ypsilantis and G. Montana · 2016
Earlier work this paper cites.
Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks
A. A. A. Setio et al · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Dropout as a Bayesian approximation: representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
Cited alongside, same era.
Bayesian convolutional neural networks with bernoulli approximate variational inference
Y. Gal and Z. Ghahramani · 2016
Cited alongside, same era.
DeepLung: 3D deep convolutional nets for automated pulmonary nodule detection and classification
W. Zhu, C. Liu, W. Fan, and X. Xie · 2017
Cited alongside, same era.
J. Ding, A. Li, Z. Hu, and L. Wangy · 2017
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Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge
A. A. A. Setio et al · 2017
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Bayesian SegNet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding
A. Kendall, V. Badrinarayanan, and R. Cipolla · 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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