Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging-Reference-Cited by-同舟云学术

Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging

Published:2020-10-14 Issue:7 Volume:31 Page:4960-4971
ISSN:0938-7994
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Wang Robin,Cai Yeyu,Lee Iris K.,Hu Rong,Purkayastha Subhanik,Pan Ian,Yi Thomas,Tran Thi My Linh,Lu Shaolei,Liu Tao,Chang Ken,Huang Raymond Y.,Zhang Paul J.,Zhang Zishu,Xiao Enhua,Wu Jing,Bai Harrison X.^ORCID

Funder

National Cancer Institute

National Institute of Biomedical Imaging and Bioengineering

National Institute of General Medical Sciences of the National Institutes of Health which funds Advance Clinical and Translational Research

Publisher

Springer Science and Business Media LLC

Subject

Radiology Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-020-07266-x.pdf

Reference43 articles.

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3. Patel MD, Ascher SM, Paspulati RM et al (2013) Managing incidental findings on abdominal and pelvic CT and MRI, part 1: white paper of the ACR incidental findings committee II on adnexal findings. J Am Coll Radiol 10(9):675–681

4. Jacobs IJ, Parmar M, Skates SJ, Menon U (2016) Ovarian cancer screening: UKCTOCS trial – authors’ reply. Lancet 387(10038):2603–2604

5. van Nagell JR Jr., Hoff JT (2013) Transvaginal ultrasonography in ovarian cancer screening: current perspectives. Int J Womens Health 6:25–33

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