Deep learning in medical imaging and radiation therapy

DOI Web Site Web Site 15 Citations
  • Berkman Sahiner
    DIDSR/OSEL/CDRH U.S. Food and Drug Administration Silver Spring MD 20993 USA
  • Aria Pezeshk
    DIDSR/OSEL/CDRH U.S. Food and Drug Administration Silver Spring MD 20993 USA
  • Lubomir M. Hadjiiski
    Department of Radiology University of Michigan Ann Arbor MI 48109 USA
  • Xiaosong Wang
    Imaging Biomarkers and Computer‐aided Diagnosis Lab Radiology and Imaging Sciences NIH Clinical Center Bethesda MD 20892‐1182 USA
  • Karen Drukker
    Department of Radiology University of Chicago Chicago IL 60637 USA
  • Kenny H. Cha
    DIDSR/OSEL/CDRH U.S. Food and Drug Administration Silver Spring MD 20993 USA
  • Ronald M. Summers
    Imaging Biomarkers and Computer‐aided Diagnosis Lab Radiology and Imaging Sciences NIH Clinical Center Bethesda MD 20892‐1182 USA
  • Maryellen L. Giger
    Department of Radiology University of Chicago Chicago IL 60637 USA

Description

<jats:p>The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.</jats:p>

Journal

Citations (15)*help

See more

Report a problem

Back to top