Multimodal deep learning fusion of ultrafast-DCE MRI and clinical information for breast lesion classification.

Journal: Computers in biology and medicine
PMID: 39978091

Abstract

BACKGROUND: Breast cancer is the most common cancer worldwide, and magnetic resonance imaging (MRI) constitutes a very sensitive technique for invasive cancer detection. When reviewing breast MRI examination, clinical radiologists rely on multimodal information, composed of imaging data but also information not present in the images such as clinical information. Most machine learning (ML) approaches are not well suited for multimodal data. However, attention-based architectures, such as Transformers, are flexible and therefore good candidates for integrating multimodal data.

Authors

  • Belinda Lokaj
    Division of Medical Information Sciences, University Hospitals of Geneva.
  • Valentin Durand de Gevigney
    Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland.
  • Dahila-Amal Djema
    Hirslanden - Clinique des Grangettes, Geneva, Switzerland.
  • Jamil Zaghir
    Division of Medical Information Sciences, University Hospitals of Geneva.
  • Jean-Philippe Goldman
    Division of Medical Information Sciences, University Hospitals of Geneva and University of Geneva, Geneva, Switzerland.
  • Mina Bjelogrlic
    Division of Medical Information Sciences, University Hospitals of Geneva and University of Geneva, Geneva, Switzerland.
  • Hugues Turbé
    Division of Medical Information Sciences, University Hospitals of Geneva.
  • Karen Kinkel
    Réseau Hospitalier Neuchâtelois, Neuchâtel, Switzerland.
  • Christian Lovis
    Division of Medical Information Sciences Geneva University Hospitals and University of Geneva.
  • Jérôme Schmid
    Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland.

Keywords

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