Deep evidential learning for radiotherapy dose prediction.

Journal: Computers in biology and medicine
Published Date:

Abstract

BACKGROUND: As we navigate towards integrating deep learning methods in the real clinic, a safety concern lies in whether and how the model can express its own uncertainty when making predictions. In this work, we present a novel application of an uncertainty-quantification framework called Deep Evidential Learning in the domain of radiotherapy dose prediction.

Authors

  • Hai Siong Tan
    Gryphon Center for Artificial Intelligence and Theoretical Sciences, Singapore; University of Pennsylvania, Perelman School of Medicine, Department of Radiation Oncology, Philadelphia, USA. Electronic address: haisiong.tan@gryphonai.com.sg.
  • Kuancheng Wang
    Georgia Institute of Technology, Atlanta, GA, USA.
  • Rafe McBeth
    Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75390, USA.