Interlenghi, M.; Salvatore, C.; Magni, V.; Caldara, G.; Schiavon, E.; Cozzi, A.; Schiaffino, S.; Carbonaro, L.A.; Castiglioni, I.; Sardanelli, F.
A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses. Diagnostics 2022, 12, 187.
https://doi.org/10.3390/diagnostics12010187
AMA Style
Interlenghi M, Salvatore C, Magni V, Caldara G, Schiavon E, Cozzi A, Schiaffino S, Carbonaro LA, Castiglioni I, Sardanelli F.
A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses. Diagnostics. 2022; 12(1):187.
https://doi.org/10.3390/diagnostics12010187
Chicago/Turabian Style
Interlenghi, Matteo, Christian Salvatore, Veronica Magni, Gabriele Caldara, Elia Schiavon, Andrea Cozzi, Simone Schiaffino, Luca Alessandro Carbonaro, Isabella Castiglioni, and Francesco Sardanelli.
2022. "A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses" Diagnostics 12, no. 1: 187.
https://doi.org/10.3390/diagnostics12010187
APA Style
Interlenghi, M., Salvatore, C., Magni, V., Caldara, G., Schiavon, E., Cozzi, A., Schiaffino, S., Carbonaro, L. A., Castiglioni, I., & Sardanelli, F.
(2022). A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses. Diagnostics, 12(1), 187.
https://doi.org/10.3390/diagnostics12010187
