Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images-Reference-Cited by-同舟云学术

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Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images

Published:2019 Issue: Volume: Page:287-296
ISSN:0302-9743
Container-title:From Bioinspired Systems and Biomedical Applications to Machine Learning
language:
Short-container-title:

Author:

Thurnhofer-Hemsi Karl^ORCID,López-Rubio Ezequiel^ORCID,Roé-Vellvé Núria,Molina-Cabello Miguel A.^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-19651-6_28

Reference14 articles.

1. Abramovich, F., Benjamini, Y., Donoho, D.L., Johnstone, I.M.: Adapting to unknown sparsity by controlling the false discovery rate. Ann. Stat. 34(2), 584–653 (2006)

2. Blueschke, D., Savin, I.: No such thing as a perfect hammer: comparing different objective function specifications for optimal control. Cent. Eur. J. Oper. Res. 25(2), 377–392 (2017)

3. Chen, X., Xu, F., Ye, Y.: Lower bound theory of nonzero entries in solutions of $$l_{2}$$ - $$l_{p}$$ minimization. SIAM J. Sci. Comput. 32(5), 2832–2852 (2010)

4. Gentile, C.: The robustness of the p-norm algorithms. Mach. Learn. 53(3), 265–299 (2003)

5. Grove, A.J., Littlestone, N., Schuurmans, D.: General convergence results for linear discriminant updates. Mach. Learn. 43(3), 173–210 (2001)

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