Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging
Link
http://link.springer.com/content/pdf/10.1186/s41747-019-0112-7.pdf
Reference27 articles.
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2. Jalalian A, Mashohor SB, Mahmud HR, Saripan MI, Ramli AR, Karasfi B (2013) Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin Imaging 37:420–426. https://doi.org/10.1016/j.clinimag.2012.09.024
3. Barr RG, Zhang Z, Cormack JB, Mendelson EB, Berg WA (2013) Probably benign lesions at screening breast US in a population with elevated risk: prevalence and rate of malignancy in the ACRIN 6666 Trial. Radiology 269:701–712. https://doi.org/10.1148/radiol.13122829
4. Zanello PA, Robim AF, Oliveira TM et al (2011) Breast ultrasound diagnostic performance and outcomes for mass lesions using Breast Imaging Reporting and Data System category 0 mammogram. Clinics (Sao Paulo) 66:443–448. https://doi.org/10.1590/S1807-59322011000300014
5. Hong AS, Rosen EL, Soo MS, Baker JA (2005) BI-RADS for sonography: positive and negative predictive values of sonographic features. AJR Am J Roentgenol 184:1260–1265. https://doi.org/10.2214/ajr.184.4.01841260
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