Deep Learning in Radiology: Does One Size Fit All?

Bradley J. Erickson, Panagiotis Korfiatis, Timothy L. Kline, Zeynettin Akkus, Kenneth Philbrick, Alexander D. Weston

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


Deep learning (DL) is a popular method that is used to perform many important tasks in radiology and medical imaging. Some forms of DL are able to accurately segment organs (essentially, trace the boundaries, enabling volume measurements or calculation of other properties). Other DL networks are able to predict important properties from regions of an image—for instance, whether something is malignant, molecular markers for tissue in a region, even prognostic markers. DL is easier to train than traditional machine learning methods, but requires more data and much more care in analyzing results. It will automatically find the features of importance, but understanding what those features are can be a challenge. This article describes the basic concepts of DL systems and some of the traps that exist in building DL systems and how to identify those traps.

Original languageEnglish (US)
Pages (from-to)521-526
Number of pages6
JournalJournal of the American College of Radiology
Issue number3
StatePublished - Mar 2018


  • Deep learning
  • computer-aided diagnosis
  • machine learning

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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