A machine learning ensemble based on radiomics to predict BI-RADS category and reduce the biopsy rate of ultrasound-detected suspicious breast masses. Interlenghi, M., Salvatore, C., Magni, V., Caldara, G., Schiavon, E., Cozzi, A., Schiaffino, S., Carbonaro, L. A., Castiglioni, I., & Sardanelli, F. Diagnostics, 12(1):187, Multidisciplinary Digital Publishing Institute, 2022.
bibtex   
@article{interlenghi2022machine,
  title={A machine learning ensemble based on radiomics to predict BI-RADS category and reduce the biopsy rate of ultrasound-detected suspicious breast masses},
  author={Interlenghi, Matteo and Salvatore, Christian and Magni, Veronica and Caldara, Gabriele and Schiavon, Elia and Cozzi, Andrea and Schiaffino, Simone and Carbonaro, Luca Alessandro and Castiglioni, Isabella and Sardanelli, Francesco},
  journal={Diagnostics},
  volume={12},
  number={1},
  pages={187},
  year={2022},
  publisher={Multidisciplinary Digital Publishing Institute}
}

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