Author:
Changyan Wang, BS,Haobo Chen, MS,Jieyi Liu, BS,Changchun Li, BS,Weiwei Jiao, BS,Qihui Guo, BS,Qi Zhang, PhD
Subject
Medical Laboratory Technology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Reference170 articles.
1. Breast cancer. World Health Organization. Available at: https://www.who.int/news-room/fact-sheets/detail/breast-cancer. (Accessed: 26 March 2021).[EB/OL].
2. Cancer fact sheets. World Health Organization. Available at: https://gco.iarc.fr/today/fact-sheets-cancers. (Accessed: 2020).[EB/OL].
3. Ciritsis A, Rossi C, Eberhard M, Marcon M, Becker AS, Boss A. Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making. Eur Radiol 2019; 29:5458-5468.
4. Benjamin L. Triche1 John T. Nelson Jr, Noah S. McGill, Kristin K. Porter, Rupan Sanyal, Franklin N. Tessler, et al. Recognizing and minimizing arti-facts at CT, MRI, US, and molecular imaging. Radiographics 2019; 39:1017-1018.
5. Eng KA, Abadeh A, Ligocki C, Lee YK, Moineddin R, Adams-Webber T, et al. Acute appendicitis: a meta-analysis of the diagnostic accuracy of US, CT, and MRI as second-line imaging tests after an initial US. Radiology 2018; 288:717-727.
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