[1]
|
Mainiero, M.B., et al. (2017) ACR Appropriateness Criteria Breast Cancer Screening. Journal of the American College of Radiology, 14, 383-390. https://doi.org/10.1016/j.jacr.2017.08.044
|
[2]
|
Friedewald, S.M., Rafferty, E.A., Rose, S.L., et al. (2014) Breast Cancer Screening Using Tomosynthesis in Combination with Digital Mammography. JAMA: Journal of the American Medical Association, 311, 2499-2507.
https://doi.org/10.1001/jama.2014.6095
|
[3]
|
Tagliafico, A.S., Calabrese, M., Bignotti, B., et al. (2017) Accuracy and Reading Time for Six Strategies Using Digital Breast Tomosynthesis in Women with Mammographically Negative Dense Breasts. European Radiology, 27, 5179- 5184. https://doi.org/10.1007/s00330-017-4918-5
|
[4]
|
Srinivasan, N., Rao, G.N. and Koteswararao, K. (2010) The Role of Pattern Recognition in Computer-Aided Diagnosis and Com-puter-Aided Detection in Medical Imaging: A Clinical Validation. International Journal of Computer Applications, 8, 6527-6532. https://doi.org/10.5120/1207-1729
|
[5]
|
Giger, M.L. (2018) Machine Learning in Medical Imaging. Journal of the American College of Radiology: JACR, 15, 512-520. https://doi.org/10.1016/j.jacr.2017.12.028
|
[6]
|
Elter, M. and Horsch, A. (2009) CADx of Mammographic Masses and Clustered Microcalcifications: A Review. Medical Physics, 36, 2052-2068. https://doi.org/10.1118/1.3121511
|
[7]
|
Freer, T.W. and Ulissey, M.J. (2001) Screening Mammography with Computer-Aided Detection: Prospective Study of 12,860 Patients in a Community Breast Center. Radiology, 220, 781-786. https://doi.org/10.1148/radiol.2203001282
|
[8]
|
Birdwell, R.L., Bandodkar, P. and Ikeda, D.M. (2005) Computer-Aided Detection with Screening Mammography in a University Hospital Setting. Radiology, 236, 451-457. https://doi.org/10.1148/radiol.2362040864
|
[9]
|
Ko, J.M., et al. (2006) Prospective Assessment of Computer-Aided Detection in Interpretation of Screening Mammography. AJR. American Journal of Roentgenology, 187, 1483-1491. https://doi.org/10.2214/AJR.05.1582
|
[10]
|
Abdelrahman, L., Ghamdi, M.A., Collado-Mesa, F., et al. (2021) Con-volutional Neural Networks for Breast Cancer Detection in Mammography: A Survey. Computers in Biology and Medi-cine, 131, Article ID: 104248.
https://doi.org/10.1016/j.compbiomed.2021.104248
|
[11]
|
Scaranelo, A.M., Eiada, R., Bukhanov, K., et al. (2012) Evaluation of Breast Amorphous Calcifications by a Computer-Aided Detection System in Full-Field Digital Mammog-raphy. The British Journal of Radiology, 85, 517-522.
https://doi.org/10.1259/bjr/31850970
|
[12]
|
Karale, V.A., Joshua, P.C., JayasreeSingh, T., et al. (2019) Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms. Journal of Digital Imaging: The Official Journal of the Society for Computer Applications in Radiology, 32, 728-745. https://doi.org/10.1007/s10278-019-00249-5
|
[13]
|
Murakami, R., Kumita, S., Tani, H., et al. (2013) Detection of Breast Cancer with a Computer-Aided Detection Applied to Full-Field Digital Mammography. Journal of Digital Imag-ing, 26, 768-773.
https://doi.org/10.1007/s10278-012-9564-5
|
[14]
|
Sadaf, A., Crystal, P., Scaranelo, A., et al. (2011) Performance of Computer-Aided Detection Applied to Full-Field Digital Mammography in Detection of Breast Cancers. European Journal of Radiology, 77, 457-461.
https://doi.org/10.1016/j.ejrad.2009.08.024
|
[15]
|
Do, Y.A., Jang, M., Yun, B.L., et al. (2021) Diagnostic Perfor-mance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography. Di-agnostics (Basel, Switzerland), 11, Article No. 1409.
https://doi.org/10.3390/diagnostics11081409
|
[16]
|
Birdwell, R.L., et al. (2001) Mammographic Characteristics of 115 Missed Cancers Later Detected with Screening Mammography and the Potential Utility of Computer-Aided Detec-tion. Radiology, 219, 192-202.
https://doi.org/10.1148/radiology.219.1.r01ap16192
|
[17]
|
Jiao, Z., Gao, X., Wang, Y., et al. (2016) A Deep Feature Based Framework for Breast Masses Classification. Neurocomputing, 197, 221-231. https://doi.org/10.1016/j.neucom.2016.02.060
|
[18]
|
Qiu, Y., Yan, S., Gundreddy, R.R., et al. (2017) A New Ap-proach to Develop Computer-Aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology. Journal of X-Ray Science and Technology, 25, 751-763.
https://doi.org/10.3233/XST-16226
|
[19]
|
Tucker, L., Gilbert, F.J., Astley, S.M., et al. (2017) Does Reader Perfor-mance with Digital Breast Tomosynthesis Vary According to Experience with Two-Dimensional Mammography? Radi-ology, 283, Article ID: 151936.
https://doi.org/10.1148/radiol.2017151936
|
[20]
|
Conant, E.F., Toledano, A.Y., Periaswamy, S., et al. (2019) Im-proving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis. Radi-ology: Artificial Intelligence, 1.
https://doi.org/10.1148/ryai.2019180096
|
[21]
|
Nazari, S.S. and Mukherjee, P. (2018) An Overview of Mammo-graphic Density and Its Association with Breast Cancer. Breast Cancer, 25, 259-267. https://doi.org/10.1007/s12282-018-0857-5
|
[22]
|
Sprague, B.L., Conant, E.F., Onega, T., et al. (2016) Variation in Mammographic Breast Density Assessments among Radiologists in Clinical Practice. Annals of Internal Medicine, 165, 457-464. https://doi.org/10.7326/M15-2934
|
[23]
|
贾田菊, 马彦云, 李延涛, 等. 基于深度学习的乳腺数字化X线BI-RADS密度分类的研究[J]. 山西医科大学学报, 2019, 50(4): 506-510.
|