We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML's accuracy with that of a breast radiologist, and verify if the radiologist's performance is improved by using ML.
Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions
Romeo, Valeria;Cuocolo, Renato;Apolito, Roberta;Stanzione, Arnaldo
;Ventimiglia, Antonio;Vitale, Annalisa;Verde, Francesco;Accurso, Antonello;Insabato, Luigi;Gencarelli, Annarita;Buonocore, Roberta;Imbriaco, Massimo;Maurea, Simone;Brunetti, Arturo
2021
Abstract
We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML's accuracy with that of a breast radiologist, and verify if the radiologist's performance is improved by using ML.File in questo prodotto:
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Clinical value of radiomics and machine learning in breast ultrasound a multicenter study for differential diagnosis of benign and malignant lesions.pdf
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