Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning-Reference-Cited by-同舟云学术

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Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning

Published:2017-11-13 Issue:1 Volume:7 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Wang Xinggang,Yang Wei,Weinreb Jeffrey,Han Juan,Li Qiubai,Kong Xiangchuang,Yan Yongluan,Ke Zan,Luo Bo,Liu Tao,Wang Liang

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/s41598-017-15720-y.pdf

Reference38 articles.

1. Prates, C., Sousa, S., Oliveira, C. & Ikram, S. Prostate metastatic bone cancer in an Egyptian Ptolemaic mummy, a proposed radiological diagnosis. International Journal of Paleopathology 1, 98–103 (2011).

2. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer Statistics, 2017. CA: a cancer journal for clinicians 67, 7–30 (2017).

3. Weinreb, J. C. et al. PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. European urology 69, 16–40 (2016).

4. Herold, C. J. et al. Imaging in the Age of Precision Medicine: Summary of the Proceedings of the 10th Biannual Symposium of the International Society for Strategic Studies in Radiology. Radiology 279, 226–238 (2016).

5. Woo, S., Suh, C. H., Kim, S. Y., Cho, J. Y. & Kim, S. H. Diagnostic Performance of Prostate Imaging Reporting and Data System Version 2 for Detection of Prostate Cancer: A Systematic Review and Diagnostic Meta-analysis. European urology (2017).

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