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

Chenyang Shen, Dan Nguyen, Zhiguo Zhou, Steve B. Jiang, Bin Dong, Xun Jia

Research output: Contribution to journalArticlepeer-review

76 Scopus citations


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.

Original languageEnglish (US)
Article number05TR01
JournalPhysics in medicine and biology
Issue number5
StatePublished - 2020


  • artificial intelligence
  • deep learning
  • deep neural network

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'An introduction to deep learning in medical physics: Advantages, potential, and challenges'. Together they form a unique fingerprint.

Cite this