Matches in SemOpenAlex for { <https://semopenalex.org/work/W2954836639> ?p ?o ?g. }
- W2954836639 endingPage "1018" @default.
- W2954836639 startingPage "998" @default.
- W2954836639 abstract "Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018." @default.
- W2954836639 created "2019-07-12" @default.
- W2954836639 creator A5010954824 @default.
- W2954836639 creator A5049693451 @default.
- W2954836639 creator A5054427605 @default.
- W2954836639 creator A5080296152 @default.
- W2954836639 date "2019-07-05" @default.
- W2954836639 modified "2023-10-12" @default.
- W2954836639 title "Machine learning in breast MRI" @default.
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