Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer.

Journal: Tomography (Ann Arbor, Mich.)
PMID: 40137578

Abstract

BACKGROUND: Predictive models like Residual Neural Networks (ResNets) can use Magnetic Resonance Imaging (MRI) data to identify cervix tumors likely to recur after radiotherapy (RT) with high accuracy. However, there persists a lack of insight into model selections (explainability). In this study, we explored whether model features could be used to generate simulated images as a method of model explainability.

Authors

  • Destie Provenzano
    School of Engineering and Applied Science, George Washington University, Washington, DC 20052, USA.
  • Jeffrey Wang
    Center for Advancement of Drug Research and Evaluation, College of Pharmacy, Western University of Health Sciences, Pomona, CA 91766, USA.
  • Sharad Goyal
    Department of Radiation Oncology, School of Medicine and Health Sciences, George Washington University, Washington, DC 20052, USA.
  • Yuan James Rao
    Department of Radiation Oncology, School of Medicine and Health Sciences, George Washington University, Washington, DC 20052, USA.

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