Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging.

Journal: Journal of neuroscience methods
Published Date: Feb 11, 2021

Abstract

BACKGROUND: Deep learning using convolutional neural networks (CNNs) has shown great promise in advancing neuroscience research. However, the ability to interpret the CNNs lags far behind, confounding their clinical translation.

Authors

  • Yunyan Zhang
    Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
  • Daphne Hong
    Departments of Schulich School of Engineering, University of Calgary, Alberta, T2N 4N1, Canada.
  • Daniel McClement
    Hotchkiss Brain Institute, University of Calgary, Alberta, T2N 4N1, Canada.
  • Olayinka Oladosu
    Hotchkiss Brain Institute, University of Calgary, Alberta, T2N 4N1, Canada; Department of Neuroscience, University of Calgary, Alberta, T2N 4N1, Canada.
  • Glen Pridham
    Hotchkiss Brain Institute, University of Calgary, Alberta, T2N 4N1, Canada.
  • Garth Slaney
    Departments of Schulich School of Engineering, University of Calgary, Alberta, T2N 4N1, Canada.

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