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Cited by in F6Publishing
For: Hickman SE, Baxter GC, Gilbert FJ. Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations. Br J Cancer 2021;125:15-22. [PMID: 33772149 DOI: 10.1038/s41416-021-01333-w] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
Number Citing Articles
1 Piruzan E, Vosoughi N, Mahdavi SR, Khalafi L, Mahani H. Target motion management in breast cancer radiation therapy. Radiol Oncol 2021;55:393-408. [PMID: 34626533 DOI: 10.2478/raon-2021-0040] [Reference Citation Analysis]
2 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]
3 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]
4 Di Maria S, Vedantham S, Vaz P. X-ray dosimetry in breast cancer screening: 2D and 3D mammography. European Journal of Radiology 2022. [DOI: 10.1016/j.ejrad.2022.110278] [Reference Citation Analysis]
5 Coppola F, Faggioni L, Gabelloni M, De Vietro F, Mendola V, Cattabriga A, Cocozza MA, Vara G, Piccinino A, Lo Monaco S, Pastore LV, Mottola M, Malavasi S, Bevilacqua A, Neri E, Golfieri R. Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging. Front Psychol 2021;12:710982. [PMID: 34650476 DOI: 10.3389/fpsyg.2021.710982] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Hickman SE, Woitek R, Le EPV, Im YR, Mouritsen Luxhøj C, Aviles-Rivero AI, Baxter GC, MacKay JW, Gilbert FJ. Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis. Radiology 2021;:210391. [PMID: 34665034 DOI: 10.1148/radiol.2021210391] [Reference Citation Analysis]
7 Takahashi K, Fujioka T, Oyama J, Mori M, Yamaga E, Yashima Y, Imokawa T, Hayashi A, Kujiraoka Y, Tsuchiya J, Oda G, Nakagawa T, Tateishi U. Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer. Tomography 2022;8:131-41. [DOI: 10.3390/tomography8010011] [Reference Citation Analysis]