Literature DB >> 33121910

A primer for understanding radiology articles about machine learning and deep learning.

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.
Copyright © 2020 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.

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


  6 in total

Review 1.  Artificial intelligence in assessment of hepatocellular carcinoma treatment response.

Authors:  Bradley Spieler; Carl Sabottke; Ahmed W Moawad; Ahmed M Gabr; Mustafa R Bashir; Richard Kinh Gian Do; Vahid Yaghmai; Radu Rozenberg; Marielia Gerena; Joseph Yacoub; Khaled M Elsayes
Journal:  Abdom Radiol (NY)       Date:  2021-03-31

Review 2.  Artificial intelligence: a critical review of current applications in pancreatic imaging.

Authors:  Maxime Barat; Guillaume Chassagnon; Anthony Dohan; Sébastien Gaujoux; Romain Coriat; Christine Hoeffel; Christophe Cassinotto; Philippe Soyer
Journal:  Jpn J Radiol       Date:  2021-02-06       Impact factor: 2.374

Review 3.  Artificial intelligence for the detection of pancreatic lesions.

Authors:  Julia Arribas Anta; Iván Martínez-Ballestero; Daniel Eiroa; Javier García; Júlia Rodríguez-Comas
Journal:  Int J Comput Assist Radiol Surg       Date:  2022-08-11       Impact factor: 3.421

Review 4.  Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine.

Authors:  Ryuji Hamamoto; Kruthi Suvarna; Masayoshi Yamada; Kazuma Kobayashi; Norio Shinkai; Mototaka Miyake; Masamichi Takahashi; Shunichi Jinnai; Ryo Shimoyama; Akira Sakai; Ken Takasawa; Amina Bolatkan; Kanto Shozu; Ai Dozen; Hidenori Machino; Satoshi Takahashi; Ken Asada; Masaaki Komatsu; Jun Sese; Syuzo Kaneko
Journal:  Cancers (Basel)       Date:  2020-11-26       Impact factor: 6.639

Review 5.  Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art.

Authors:  Maxime Barat; Anne-Ségolène Cottereau; Sébastien Gaujoux; Florence Tenenbaum; Mathilde Sibony; Jérôme Bertherat; Rossella Libé; Martin Gaillard; Anne Jouinot; Guillaume Assié; Christine Hoeffel; Philippe Soyer; Anthony Dohan
Journal:  Cancers (Basel)       Date:  2022-01-23       Impact factor: 6.639

Review 6.  The Role of Magnetic Resonance Imaging in the Management of Esophageal Cancer.

Authors:  Anna Pellat; Anthony Dohan; Philippe Soyer; Julie Veziant; Romain Coriat; Maximilien Barret
Journal:  Cancers (Basel)       Date:  2022-02-23       Impact factor: 6.639

  6 in total

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