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Cited by in F6Publishing
For: Hinton B, Ma L, Mahmoudzadeh AP, Malkov S, Fan B, Greenwood H, Joe B, Lee V, Kerlikowske K, Shepherd J. Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study. Cancer Imaging 2019;19:41. [PMID: 31228956 DOI: 10.1186/s40644-019-0227-3] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Ha R, Jairam MP. A review of artificial intelligence in mammography. Clinical Imaging 2022. [DOI: 10.1016/j.clinimag.2022.05.005] [Reference Citation Analysis]
2 Li X, Zhou Y, Du P, Lang G, Xu M, Wu W. A deep learning system that generates quantitative CT reports for diagnosing pulmonary Tuberculosis. Appl Intell 2021;51:4082-93. [DOI: 10.1007/s10489-020-02051-1] [Cited by in Crossref: 7] [Article Influence: 3.5] [Reference Citation Analysis]
3 Rehman KU, Li J, Pei Y, Yasin A. A review on machine learning techniques for the assessment of image grading in breast mammogram. Int J Mach Learn & Cyber . [DOI: 10.1007/s13042-022-01546-2] [Reference Citation Analysis]
4 Mello-Thoms C. The Path to Implementation of Artificial Intelligence in Screening Mammography Is Not All That Clear. JAMA Netw Open 2020;3:e200282. [PMID: 32119092 DOI: 10.1001/jamanetworkopen.2020.0282] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
5 Katunin P, Zhou J, Shehata OM, Peden AA, Cadby A, Nikolaev A. An Open-Source Framework for Automated High-Throughput Cell Biology Experiments. Front Cell Dev Biol 2021;9:697584. [PMID: 34631697 DOI: 10.3389/fcell.2021.697584] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Gastounioti A, Desai S, Ahluwalia VS, Conant EF, Kontos D. Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review. Breast Cancer Res 2022;24:14. [PMID: 35184757 DOI: 10.1186/s13058-022-01509-z] [Reference Citation Analysis]
7 Zhu X, Wolfgruber TK, Leong L, Jensen M, Scott C, Winham S, Sadowski P, Vachon C, Kerlikowske K, Shepherd JA. Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women. Radiology 2021;:203758. [PMID: 34491131 DOI: 10.1148/radiol.2021203758] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Wanders AJT, Mees W, Bun PAM, Janssen N, Rodríguez-Ruiz A, Dalmış MU, Karssemeijer N, van Gils CH, Sechopoulos I, Mann RM, van Rooden CJ. Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms. Radiology 2022;:210832. [PMID: 35133194 DOI: 10.1148/radiol.210832] [Reference Citation Analysis]