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[Crossref]

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[Crossref]
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[Crossref]
[PubMed]

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[PubMed]

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[Crossref]

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[Crossref]
[PubMed]

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[Crossref]

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[Crossref]
[PubMed]

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[Crossref]

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[Crossref]
[PubMed]

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[Crossref]
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[Crossref]

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[Crossref]
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[Crossref]
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[Crossref]
[PubMed]

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[Crossref]
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[Crossref]
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[Crossref]
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[Crossref]

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[Crossref]
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[Crossref]
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[Crossref]

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[Crossref]
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[Crossref]

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).

[Crossref]
[PubMed]

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[Crossref]
[PubMed]

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[Crossref]

J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).

[Crossref]
[PubMed]

W. Li, L. Duan, D. Xu, and I. W. Tsang, “Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1134–1148 (2014).

[Crossref]
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J. Xu, L. Xiang, Q. Liu, H. Gilmore, J. Wu, J. Tang, and A. Madabhushi, “Stacked sparse autoencoder (SSDA) for nuclei detection of breast cancer histopathology images,” IEEE Trans. Med. Imaging 35(1), 119–130 (2016).

[Crossref]
[PubMed]

Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012).

[Crossref]
[PubMed]

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[Crossref]
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[Crossref]

Y. Zhang, Y. Xi, Q. Yang, W. Cong, J. Zhou, and G. Wang, “Spectral CT reconstruction with image sparsity and spectral mean,” IEEE Trans. Comput. Imaging 2(4), 510–523 (2016).

[Crossref]

Y. Chen, Z. Yang, Y. Hu, G. Yang, Y. Zhu, Y. Li, L. Luo, W. Chen, and C. Toumoulin, “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Phys. Med. Biol. 57(9), 2667–2688 (2012).

[Crossref]
[PubMed]

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[Crossref]
[PubMed]

Y. Chen, X. Yin, L. Shi, H. Shu, L. Luo, J.-L. Coatrieux, and C. Toumoulin, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Phys. Med. Biol. 58(16), 5803–5820 (2013).

[Crossref]
[PubMed]

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[Crossref]
[PubMed]

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[Crossref]

Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray CT reconstruction via dictionary learning,” IEEE Trans. Med. Imaging 31(9), 1682–1697 (2012).

[Crossref]
[PubMed]

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[Crossref]
[PubMed]

A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36(11), 4911–4919 (2009).

[Crossref]
[PubMed]

J. Zhu, N. Chen, H. Perkins, and B. Zhang, “Gibbs max-margin topic models with data augmentation,” J. Mach. Learn. Res. 15(1), 1073–1110 (2013).

J. Ma, H. Zhang, Y. Gao, J. Huang, Z. Liang, Q. Feng, and W. Chen, “Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior,” Phys. Med. Biol. 57(22), 7519–7542 (2012).

[Crossref]
[PubMed]

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[Crossref]
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[Crossref]

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[Crossref]

Y. Zhang, Y. Wang, W. Zhang, F. Lin, Y. Pu, and J. Zhou, “Statistical iterative reconstruction using adaptive fractional order regularization,” Biomed. Opt. Express 7(3), 1015–1029 (2016).

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[Crossref]

J.-F. Cai, X. Jia, H. Gao, S. B. Jiang, Z. Shen, and H. Zhao, “Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study,” IEEE Trans. Med. Imaging 33(8), 1581–1591 (2014).

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