Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?

Properly trained CNNs with transfer learning are highly effective in differentiating benign and malignant lesions on breast ultrasound. Setting clinical thresholds increased sensitivity. CNNs might be useful as support tools in managing suspicious lesions on breast ultrasound.

Authors

Vincenzo Ricciardi (ORCID: https://orcid.org/0000-0002-4599-8746)
Daniela Origgi (ORCID: https://orcid.org/0000-0003-1338-312X)
Enrico Cassano (ORCID: https://orcid.org/0000-0002-3437-8939)
Marta Montesano
P. De Marco (ORCID: https://orcid.org/0000-0001-8264-7219)

Institutions

University of Milan (IT)
European Institute of Oncology (IT)

Publication Details

Journal: European Radiology Experimental
Published: 2024-10-28
DOI: https://doi.org/10.1186/s41747-024-00480-y
Citations: 2
Primary Topic: AI in cancer detection
Type: article
Field-Weighted Citation Impact: 0.69

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article

Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?

Vincenzo Ricciardi, Daniela Origgi, Enrico Cassano, Marta Montesano et al.

Oct 28, 2024

2 citations

European Radiology Experimental

AI in cancer detection

0.69

article

Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?

Vincenzo Ricciardi, Daniela Origgi, Enrico Cassano, Marta Montesano, P. De Marco

October 28, 2024article en

2 citations

Abstract

Properly trained CNNs with transfer learning are highly effective in differentiating benign and malignant lesions on breast ultrasound. Setting clinical thresholds increased sensitivity. CNNs might be useful as support tools in managing suspicious lesions on breast ultrasound.

European Radiology ExperimentalVol. 8(1)

University of Milan (IT), European Institute of Oncology (IT)

Reduced inequalities

Openalex Percentile: Top 24%

AI in cancer detection

0.69

DOI Open Access PDF OpenAlex

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