Radiomics and artificial intelligence in breast imaging: a survey-Reference-Cited by-同舟云学术

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Radiomics and artificial intelligence in breast imaging: a survey

Published:2023-07-08 Issue:S1 Volume:56 Page:857-892
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Zhang Tianyu,Tan Tao,Samperna Riccardo,Li Zhang,Gao Yuan,Wang Xin,Han Luyi,Yu Qifeng,Beets-Tan Regina G. H.,Mann Ritse M.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-023-10543-y.pdf

Reference186 articles.

1. Aerts HJ (2016) The potential of Radiomic-Based phenotyping in Precision Medicine: a review. JAMA Oncol 2(12):1636–1642. https://doi.org/10.1001/jamaoncol.2016.2631

2. Arefan D, Chai R, Sun M et al (2020a) Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features. Med Phys 47(12):6334–6342

3. Arefan D, Mohamed AA, Berg WA et al (2020b) Deep learning modeling using normal mammograms for predicting breast cancer risk. Med Phys 47(1):110–118

4. Beau A-B, Andersen PK, Vejborg I et al (2018) Limitations in the effect of screening on breast cancer mortality. J Clin Oncol 36(30):2988

5. Becker AS, Marcon M, Ghafoor S et al (2017) Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest Radiol 52(7):434–440

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