| Literature DB >> 33121910 |
Takeshi Nakaura1, Toru Higaki2, Kazuo Awai2, Osamu Ikeda3, Yasuyuki Yamashita3.
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.Keywords: Deep learning; Machine learning; Magnetic resonance imaging; Tomography,; X-ray computed
Mesh:
Year: 2020 PMID: 33121910 DOI: 10.1016/j.diii.2020.10.001
Source DB: PubMed Journal: Diagn Interv Imaging ISSN: 2211-5684 Impact factor: 4.026