A primer for understanding radiology articles about machine learning and deep learning - 26/11/20
, Toru Higaki b, Kazuo Awai b, Osamu Ikeda a, Yasuyuki Yamashita a
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Highlights |
• | There are many machine learning methods and no model works best for all situations. |
• | Texture analysis differs from histogram analysis in that it can evaluate spatial relationships. |
• | Deep learning can approximate all functions with high accuracy. |
• | One must carefully evaluate whether the performance of machine learning is just over-fitting or true performance. |
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.
Le texte complet de cet article est disponible en PDF.Keywords : Machine learning, Deep learning, Tomography,, X-ray computed, Magnetic resonance imaging
Abbreviations : ADC, AI, CNN, CT, GLCM, GLDM, GLRLM, GLSZM, LSTM, NGTDM, RNN, ROI, VOI
Plan
Vol 101 - N° 12
P. 765-770 - décembre 2020 Retour au numéro
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