Explainability of radiomics through formal methods.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 19, 2022

Abstract

BACKGROUND AND OBJECTIVE: Artificial Intelligence has proven to be effective in radiomics. The main problem in using Artificial Intelligence is that researchers and practitioners are not able to know how the predictions are generated. This is currently an open issue because results' explainability is advantageous in understanding the reasoning behind the model, both for patients than for implementing a feedback mechanism for medical specialists using decision support systems.

Authors

  • Giulia Varriano
    Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy. Electronic address: giulia.varriano@unimol.it.
  • Pasquale Guerriero
    Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy. Electronic address: pasquale.guerriero@unimol.it.
  • Antonella Santone
    Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy.
  • Francesco Mercaldo
    Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy; Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy. Electronic address: francesco.mercaldo@iit.cnr.it.
  • Luca Brunese
    Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy.

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