Deep Learning with Transfer Learning on Digital Breast Tomosynthesis: A Radiomics-Based Model for Predicting Breast Cancer Risk

Deep Learning with Transfer Learning on Digital Breast Tomosynthesis: A Radiomics-Based Model for Predicting Breast Cancer Risk

<b>Background</b>: Digital breast tomosynthesis (DBT) is a valuable imaging modality for breast cancer detection; however, its interpretation remains time-consuming and subject to inter-reader variability. This study aimed to develop and evaluate two deep learning (DL) models based on tr...

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Bibliographic Details
Main Authors: Francesca Galati, Roberto Maroncelli, Chiara De Nardo, Lucia Testa, Gloria Barcaroli, Veronica Rizzo, Giuliana Moffa, Federica Pediconi
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Diagnostics
Subjects:
digital breast tomosynthesis
deep learning
transfer learning
breast cancer risk stratification
radiomics
Online Access:https://www.mdpi.com/2075-4418/15/13/1631
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