Deep Learning for Breast Cancer Screening Using Diffusion-Weighted MRI
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Pablo García Marcos [1] ; Paula Puerta González [1] ; Sara Fernández Arias [1] ; Rebeca Oliveira Suárez [1] ; Guillermo Lorenzo [3] ; Héctor Gómez [2] ; Covadonga del Camino [4] ; Ángel Río-Álvarez [1] ; Víctor M. González [1]
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[1] Universidad de Oviedo
Universidad de Oviedo
Oviedo, España
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[2] Purdue University
Purdue University
Township of Wabash, Estados Unidos
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[3] The University of Texas
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[4] Oviedo Central University Hospital
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- Localización: CASEIB 2025. Libro de Actas del XLIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica: Innovando en tecnología para la salud y calidad de vida / David Hernando (ed. lit.), Jesús Lazaro Plaza (ed. lit.), Pablo Laguna Lasaosa (aut.)
, Juan Pablo Martínez Cortés (aut.) , 2025, ISBN 978-84-09-80259-3, págs. 68-71 - Idioma: inglés
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Resumen
Cancer remains a leading cause of global mortality, with breast cancer showing particularly high incidence among women. Early detection significantly improves prognosis. Diagnosis and monitoring are commonly performed either using mammography or dynamic contrast-enhanced magnetic resonance imaging. Recently, diffusion-weighted magnetic resonance imaging has gathered increased attention as an alternative to both techniques due to some key advantages, although it has not yet achieved widespread use in clinical workflows, as diffusion weighted images (DWI) present limitations that harden the extraction of information from them. Recent advances in artificial intelligence, especially in the field of computer vision, offer opportunities to improve and automate information extraction from DWI. In this study, we explore the use of DWI in screening workflows with the creation and evaluation of a set of classification algorithms. The results obtained by our algorithms display the potential of deep learning applied to DWI, achieving an F1 score of 84% and a recall of nearly 90%.
