Engineering the Image Representation for Deep Learning in Contrast-Enhanced Mammography: A Systematic Analysis of Preprocessing and Anatomical Masking

Deep-learning models applied to contrast-enhanced mammography (CEM) are known to be highly sensitive to the input image representation. However, preprocessing is often treated as a secondary step and rarely analyzed as an independent design variable. In this work, we present a systematic engineering...

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Autors principals:	Roberta Fusco, Vincenza Granata, Paolo Vallone, Teresa Petrosino, Maria Daniela Iasevoli, Mauro Mattace Raso, Davide Pupo, Piero Trovato, Igino Simonetti, Paolo Pariante, Vincenzo Cerciello, Gerardo Ferrara, Modesta Longobucco, Giulia Capuano, Roberto Morcavallo, Caterina Todisco, Fabiana Antenucci, Mario Sansone, Daniele La Forgia, Antonella Petrillo
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2026-03-01
Col·lecció:	Bioengineering
Matèries:	oncology breast deep learning convolutional neural networks
Accés en línia:	https://www.mdpi.com/2306-5354/13/3/322
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