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Convolutional neural networks have been applied to a wide variety of computer vision tasks.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Pattern Recognition and Machine Learning (Information Science and Statistics)
C. M. Bishop · 2006
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Scene segmentation with conditional random fields learned from partially labeled images
J. Verbeek and B. Triggs · 2007
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Efficient inference in fully connected crfs with gaussian edge potentials
P. Krähenbühl and V. Koltun · 2011
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Adaptive deconvolutional networks for mid and high level feature learning
M. D. Zeiler, G. W. Taylor, and R. Fergus · 2011
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Deep neural networks segment neuronal membranes in electron microscopy images
D. C. Cireşan, A. Giusti, L. M. Gambardella, and J. Schmidhuber · 2012
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Human hand modelling: kinematics, dynamics, applications
A. Gustus, G. Stillfried, J. Visser, H. Jörntell, and P. van der Smagt · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2013
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Closed-form training of conditional random fields for large scale image segmentation
A. Kolesnikov, M. Guillaumin, V. Ferrari, and C. H. Lampert · 2014
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C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2014
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H. Li, R. Zhao, and X. Wang · 2014
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M. Lin, Q. Chen, and S. Yan · 2014
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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
B. Menze, A. Jakab, S. Bauer, et al · 2014
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Accurate image super-resolution using very deep convolutional networks
J. Kim, J. K. Lee, and K. M. Lee · 2015
Cited alongside, same era.
Dcan: Deep contour-aware networks for accurate gland segmentation
H. Chen, X. Qi, L. Yu, and P.-A. Heng · 2016
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Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
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3d deeply supervised network for automatic liver segmentation from ct volumes
Q. Dou, H. Chen, Y. Jin, L. Yu, J. Qin, and P.-A. Heng · 2016
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The importance of skip connections in biomedical image segmentation
M. Drozdzal, E. Vorontsov, G. Chartrand, S. Cadoury, and C. Pal · 2016
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Superpixel convolutional networks using bilateral inceptions
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D. P. Kingma and J. L. Ba · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhammer, and T. Darrell · 2015
Cited alongside, same era.
Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
Cited alongside, same era.
Deep convolutional neural networks for the segmentation of gliomas in multi-sequence mri
S. Pereira, A. Pinto, V. Alves, and C. A. Silva · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, et al · 2015
Cited alongside, same era.
R. Gadde, V. Jampani, M. Kiefel, D. Kappler, and P. V. Gehler · 2016
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2016
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Brain tumor segmentation with deep neural networks
M. Havaei, A. Davy, D. Warde-Farley, et al · 2016
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Deep learning trends for focal brain pathology segmentation in mri
M. Havaei, N. Guizard, P.-M. Jodoin, and H. Larochelle · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Efficient multi-scale 3d cnn with fully-connected crf for accurate brain lesion segmentation
K. Kamnitsas, C. Ledig, V. F. Newcombe, et al · 2016
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Isles 2015 - a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral mri
O. Maier, B. H. Menze, J. von der Gablentz, et al · 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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Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
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3d u-net: Learning dense volumetric segmentation from sparse annotation
Özgün Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger · 2016
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