title: Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.
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.
authors.affiliation: Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands. b.vanginneken@radboudumc.nl.
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D000069550:Machine Learning; D013902:Radiography, Thoracic
keywords: Computer-aided detection; Computer-aided diagnosis; Deep learning; Image processing; Machine learning; Pulmonary image analysis