Deep Learning in Radiology

Morgan P. McBee, Omer A. Awan, Andrew T. Colucci, Comeron W. Ghobadi, Nadja Kadom, Akash P. Kansagra, Srini Tridandapani, William F. Auffermann

Research output: Contribution to journalReview articlepeer-review

172 Scopus citations

Abstract

As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data processing techniques. One such technique, deep learning (DL), has become a remarkably powerful tool for image processing in recent years. In this work, the Association of University Radiologists Radiology Research Alliance Task Force on Deep Learning provides an overview of DL for the radiologist. This article aims to present an overview of DL in a manner that is understandable to radiologists; to examine past, present, and future applications; as well as to evaluate how radiologists may benefit from this remarkable new tool. We describe several areas within radiology in which DL techniques are having the most significant impact: lesion or disease detection, classification, quantification, and segmentation. The legal and ethical hurdles to implementation are also discussed. By taking advantage of this powerful tool, radiologists can become increasingly more accurate in their interpretations with fewer errors and spend more time to focus on patient care.

Original languageEnglish
Pages (from-to)1472-1480
Number of pages9
JournalAcademic radiology
Volume25
Issue number11
DOIs
StatePublished - Nov 2018

Keywords

  • Machine learning
  • artificial intelligence
  • deep learning
  • machine intelligence

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