Rad4XCNN: A new agnostic method for post-hoc global explanation of CNN-derived features by means of Radiomics.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 7, 2025

Abstract

BACKGROUND AND OBJECTIVE: In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. However, it appears that explainability is inversely proportional to accuracy. For this reason, achieving transparency without compromising predictive accuracy remains a key challenge.

Authors

  • Francesco Prinzi
    Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.
  • Carmelo Militello
    Institute for High-Performance Computing and Networking (ICAR-CNR), Italian National Research Council, Palermo, Italy.
  • Calogero Zarcaro
    Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.
  • Tommaso Vincenzo Bartolotta
    Department of Radiology, Fondazione Istituto G. Giglio, Ct.da Pietrapollastra, Cefalù, Palermo, Italy.
  • Salvatore Gaglio
    CNR-ICAR, National Research Council of Italy, Via Ugo La Malfa, 153, Palermo, Italy.
  • Salvatore Vitabile
    Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy. salvatore.vitabile@unipa.it.

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