An introduction to deep learning in medical physics: advantages, potential, and challenges.

Journal: Physics in medicine and biology
Published Date:

Abstract

As one of the most popular approaches in artificial intelligence, deep learning (DL) has attracted a lot of attention in the medical physics field over the past few years. The goals of this topical review article are twofold. First, we will provide an overview of the method to medical physics researchers interested in DL to help them start the endeavor. Second, we will give in-depth discussions on the DL technology to make researchers aware of its potential challenges and possible solutions. As such, we divide the article into two major parts. The first part introduces general concepts and principles of DL and summarizes major research resources, such as computational tools and databases. The second part discusses challenges faced by DL, present available methods to mitigate some of these challenges, as well as our recommendations.

Authors

  • Chenyang Shen
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75002, USA.
  • Dan Nguyen
    University of Massachusetts Chan Medical School, Worcester, Massachusetts.
  • Zhiguo Zhou
  • Steve B Jiang
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75235.
  • Bin Dong
    Ricoh Software Research Center (Beijing), Beijing, China.
  • Xun Jia
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75235.