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Acad Radiol. 2018 Nov;25(11):1472-1480. doi: 10.1016/j.acra.2018.02.018. Epub 2018 Mar 30.

Deep Learning in Radiology.

Author information

1
Department of Radiology and Medical Imaging, Cincinnati Children's Hospital, Cincinnati, Ohio.
2
Department of Radiology, Temple University Hospital, Philadelphia, Pennsylvania.
3
Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
4
Department of Radiology, University of Chicago, Chicago, Illinois.
5
Department of Radiology and Imaging Sciences, Children's Healthcare of Atlanta (Egleston), Emory University School of Medicine, Atlanta, Georgia.
6
Mallinckrodt Institute of Radiology and Departments of Neurological Surgery and Neurology, Washington University School of Medicine, Saint Louis, Missouri.
7
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia.
8
Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1365 Clifton Road NE, Atlanta, GA 30322. Electronic address: william.auffermann@hsc.utah.edu.

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.

KEYWORDS:

Machine learning; artificial intelligence; deep learning; machine intelligence

PMID:
29606338
DOI:
10.1016/j.acra.2018.02.018

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