[HTML][HTML] A primer for understanding radiology articles about machine learning and deep learning
Diagnostic and Interventional Imaging, 2020•Elsevier
The application of machine learning and deep learning in the field of imaging is rapidly
growing. Although the principles of machine and deep learning are unfamiliar to the majority
of clinicians, the basics are not so complicated. One of the major issues is that commentaries
written by experts are difficult to understand, and are not primarily written for clinicians. The
purpose of this article was to describe the different concepts behind machine learning,
radiomics, and deep learning to make clinicians more familiar with these techniques.
growing. Although the principles of machine and deep learning are unfamiliar to the majority
of clinicians, the basics are not so complicated. One of the major issues is that commentaries
written by experts are difficult to understand, and are not primarily written for clinicians. The
purpose of this article was to describe the different concepts behind machine learning,
radiomics, and deep learning to make clinicians more familiar with these techniques.
Abstract
The application of machine learning and deep learning in the field of imaging is rapidly growing. Although the principles of machine and deep learning are unfamiliar to the majority of clinicians, the basics are not so complicated. One of the major issues is that commentaries written by experts are difficult to understand, and are not primarily written for clinicians. The purpose of this article was to describe the different concepts behind machine learning, radiomics, and deep learning to make clinicians more familiar with these techniques.
Elsevier
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