Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning

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Title:	Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning
Author(s):	Ginneken, B. van
Publication year:	2017
Source:	Radiological Physics and Technology, vol. 10, iss. 1, (2017), pp. 23-32
ISSN:	1865-0333
DOI:	https://doi.org/10.1007/s12194-017-0394-5
Publication type:	Article / Letter to editor
Please use this identifier to cite or link to this item : https://hdl.handle.net/2066/173102
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Subject:	Radboudumc 9: Rare cancers RIHS: Radboud Institute for Health Sciences
Organization:	Medical Imaging
Journal title:	Radiological Physics and Technology
Volume:	vol. 10
Issue:	iss. 1
Page start:	p. 23
Page end:	p. 32
Abstract:	Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.