Role of sureness in evaluating AI/CADx: Lesion-based repeatability of machine learning classification performance on breast MRI.

Journal: Medical physics
Published Date: Aug 21, 2023

Abstract

BACKGROUND: Artificial intelligence/computer-aided diagnosis (AI/CADx) and its use of radiomics have shown potential in diagnosis and prognosis of breast cancer. Performance metrics such as the area under the receiver operating characteristic (ROC) curve (AUC) are frequently used as figures of merit for the evaluation of CADx. Methods for evaluating lesion-based measures of performance may enhance the assessment of AI/CADx pipelines, particularly in the situation of comparing performances by classifier.

Authors

  • Heather M Whitney
    Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Ave., Chicago, IL, MC202660637, USA.
  • Karen Drukker
    Department of Radiology, University of Chicago, Chicago, IL, 60637, USA.
  • Michael Vieceli
    Department of Physics, Wheaton College, Wheaton, Illinois, USA.
  • Amy Van Dusen
    Department of Physics, Wheaton College, Wheaton, Illinois, USA.
  • Michelle de Oliveira
    Department of Physics, Wheaton College, Wheaton, Illinois, USA.
  • Hiroyuki Abe
    Department of Pathology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
  • Maryellen L Giger
    Department of Radiology, University of Chicago, 5841 S Maryland Ave., Chicago, IL, 60637, USA.

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