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Mi-S1.1-IAIM1: Inteligencia artificial en imagen médica (I) Lugar: Salón de Actos Presidente de la sesión: Andrés Santos Lleó Presidente de la sesión: Julia Ramírez García | |
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12:45 - 13:00
Deep Learning for Breast Cancer Screening Using Diffusion-Weighted MRI 1: Computer Science Department, University of Oviedo, Spain; 2: Electrical Engineering Department, University of Oviedo, Spain; 3: Biomedical Engineering Center, University of Oviedo, Spain; 4: School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907, USA; 5: Purdue Center for Cancer Research, Purdue University, West Lafayette, IN, USA; 6: Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Drive, West Lafayette, IN 47907, USA; 7: Group of Numerical Methods in Engineering, Department of Mathematics, Campus de Elvi˜na s/n, 15071 A Coru˜na, Galicia, Spain; 8: Oden Institute for Computational Engineering and Sciences, The University of Texas, 201 E. 24th Street, Austin, TX 78712-1229, USA; 9: Radiodiagnostic Service, Oviedo Central University Hospital (HUCA), Oviedo, Spain 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%.
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