Deep nets vs expert designed features in medical physics: An IMRT QA case study.

Journal: Medical physics
Published Date: Jun 1, 2018

Abstract

PURPOSE: The purpose of this study was to compare the performance of Deep Neural Networks against a technique designed by domain experts in the prediction of gamma passing rates for Intensity Modulated Radiation Therapy Quality Assurance (IMRT QA).

Authors

  • Yannet Interian
    Data Analytic Program, University of San Francisco, San Francisco, California.
  • Vincent Rideout
    MS in Analytics Program, University of San Francisco, San Francisco, CA, USA.
  • Vasant P Kearney
    Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
  • Efstathios Gennatas
    Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
  • Olivier Morin
    Department of Radiation Oncology, University of California, San Francisco, California.
  • Joey Cheung
    Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
  • Timothy Solberg
    Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
  • Gilmer Valdes
    Department of Radiation Oncology, University of California, San Francisco, California.

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