Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning
By B. van Ginneken. 2017
175 citations recorded by the source corpus.
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.
Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Pulmonary and Respiratory Medicine
In Radiology, Nuclear Medicine and Imaging
- Arvid Lundervold
- Taco Cohen
- Marysia Winkels
- Maurice Weiler
- Erik J. Bekkers
- Gabriele Cesa
- Florian Tramèr
- Dan Boneh
Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [101],24 covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [102–111], including radiomics and imaging geno”
- 3D G-CNNs for Pulmonary Nodule Detection (2018, background): “With regards to pulmonary nodule detection specifically, deep learning techniques for candidate generation and false positive reduction unambiguously outperform classical machine learning approaches (12; 13; 14).”