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.

Keywords: Computer-aided detection; Computer-aided diagnosis; Deep learning; Image processing; Machine learning; Pulmonary image analysis.

Fig. 1

Typical example of a convolutional network. This network was used to analyze three 32 × 32 patches extracted from chest CT scans that can either represent a true airway branch or a leakage. This architecture was used in [20]

Fig. 2

Top: setup for a “traditional” CAD system for nodule detection in CT. Bottom: plugging in convnets to perform false positive reduction