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Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare.
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A randomized ensemble approach to industrial ct segmentation
H. Kim, J. J. Thiagarajan, and P.-T. Bremer · 2015
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Timbir: A method for time-space reconstruction from interlaced views
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Few-Views Image Reconstruction with SMART and an Allowance for Contrast Structure Shadows
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
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Few-view ct reconstruction via a novel non-local means algorithm
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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A deep convolutional neural network using directional wavelets for low-dose x-ray ct reconstruction
E. Kang, J. Min, and J. C. Ye · 2016
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Few-view ct reconstruction method based on deep learning
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One network to solve them all—solving linear inverse problems using deep projection models
J. Chang, C.-L. Li, B. Póczos, B. Kumar, and A. C. Sankaranarayanan · 2017
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Low-dose ct via convolutional neural network
H. Chen, Y. Zhang, W. Zhang, P. Liao, K. Li, J. Zhou, and G. Wang · 2017
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Framing u-net via deep convolutional framelets: Application to sparse-view ct
Y. Han and J. C. Ye · 2017
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Restoration of missing data in limited angle tomography based on helgason-ludwig consistency conditions
Y. Huang, X. Huang, O. Taubmann, Y. Xia, V. Haase, J. Hornegger, G. Lauritsch, and A. Maier · 2017
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Deep convolutional neural network for inverse problems in imaging
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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