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

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 techn...

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Detalles Bibliográficos
Autor principal: van Ginneken, Bram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Japan 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5337239/
https://www.ncbi.nlm.nih.gov/pubmed/28211015
http://dx.doi.org/10.1007/s12194-017-0394-5
Descripción
Sumario: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.