a data augmentation strategy to narrow the robustness gap between expert radiologists and deep learning classifiers.

Journal: Frontiers in radiology
Published Date: Dec 19, 2024

Abstract

PURPOSE: Successful performance of deep learning models for medical image analysis is highly dependent on the quality of the images being analysed. Factors like differences in imaging equipment and calibration, as well as patient-specific factors such as movements or biological variability (e.g., tissue density), lead to a large variability in the quality of obtained medical images. Consequently, robustness against the presence of noise is a crucial factor for the application of deep learning models in clinical contexts.

Authors

  • Luc Lerch
    Medical Image Analysis Group, ARTORG Centre for Biomedical Research, University of Bern, Bern, Switzerland.
  • Lukas S Huber
    Cognition, Perception and Research Methods, Department of Psychology, University of Bern, Bern, Switzerland.
  • Amith Kamath
    Medical Image Analysis Group, ARTORG Centre for Biomedical Research, University of Bern, Bern, Switzerland.
  • Alexander Pöllinger
    Department of Diagnostic, Interventional, and Pediatric Radiology, Inselspital Bern, University of Bern, Bern, Switzerland.
  • Aurélie Pahud de Mortanges
    Medical Image Analysis Group, ARTORG Centre for Biomedical Research, University of Bern, Bern, Switzerland.
  • Verena C Obmann
    Department of Diagnostic, Interventional, and Pediatric Radiology, Inselspital Bern, University of Bern, Bern, Switzerland.
  • Florian Dammann
    Department of Diagnostic, Interventional, and Pediatric Radiology, Inselspital Bern, University of Bern, Bern, Switzerland.
  • Walter Senn
    Computational Neuroscience Group, Department of Physiology, University of Bern, Bern, Switzerland.
  • Mauricio Reyes
    Center for Artificial Intelligence in Medicine, University of Bern, Bern, Switzerland.

Keywords

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