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

被引：81
作者：
Nakaura, Takeshi [1 ]
Higaki, Toru [2 ,3 ]
Awai, Kazuo [2 ,3 ]
Ikeda, Osamu [1 ]
Yamashita, Yasuyuki [1 ]
机构：
[1] Japan Kumamoto Univ, Grad Sch Med Sci, Dept Diagnost Radiol, Chuo Ku, 1-1-1 Honjo, Kumamoto 8608556, Japan
[2] Hiroshima Univ, Dept Diagnost Radiol, Minami Ku, 1-2-3 Kasumi, Hiroshima 7348551, Japan
[3] Hiroshima Univ, Dept Radiol, Minami Ku, 1-2-3 Kasumi, Hiroshima 7348551, Japan
来源：
DIAGNOSTIC AND INTERVENTIONAL IMAGING | 2020年 / 101卷 / 12期
关键词：
Machine learning; Deep learning; Tomography; X-ray computed; Magnetic resonance imaging; APPROXIMATE; RADIOMICS; ALGORITHM; BENIGN; IMAGES;
D O I：
10.1016/j.diii.2020.10.001
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
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. (C) 2020 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.
引用
收藏
页码：765 / 770
页数：6
相关论文
共 41 条
  • [1] Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest
    Akai, H.
    Yasaka, K.
    Kunimatsu, A.
    Nojima, M.
    Kokudo, T.
    Kokudo, N.
    Hasegawa, K.
    Abe, O.
    Ohtomo, K.
    Kiryu, S.
    [J]. DIAGNOSTIC AND INTERVENTIONAL IMAGING, 2018, 99 (10) : 643 - 651
  • [2] Data mining with decision trees and decision rules
    Apte, C
    Weiss, S
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 1997, 13 (2-3): : 197 - 210
  • [3] An optimal algorithm for approximate nearest neighbor searching in fixed dimensions
    Arya, S
    Mount, DM
    Netanyahu, NS
    Silverman, R
    Wu, AY
    [J]. JOURNAL OF THE ACM, 1998, 45 (06) : 891 - 923
  • [4] Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers-From the Radiology Editorial Board
    Bluemke, David A.
    Moy, Linda
    Bredella, Miriam A.
    Ertl-Wagner, Birgit B.
    Fowler, Kathryn J.
    Goh, Vicky J.
    Halpern, Elkan F.
    Hess, Christopher P.
    Schiebler, Mark L.
    Weiss, Clifford R.
    [J]. RADIOLOGY, 2020, 294 (03) : 487 - 489
  • [5] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [6] SUPPORT-VECTOR NETWORKS
    CORTES, C
    VAPNIK, V
    [J]. MACHINE LEARNING, 1995, 20 (03) : 273 - 297
  • [7] Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network
    Fujioka, Tomoyuki
    Kubota, Kazunori
    Mori, Mio
    Kikuchi, Yuka
    Katsuta, Leona
    Kasahara, Mai
    Oda, Goshi
    Ishiba, Toshiyuki
    Nakagawa, Tsuyoshi
    Tateishi, Ukihide
    [J]. JAPANESE JOURNAL OF RADIOLOGY, 2019, 37 (06) : 466 - 472
  • [8] Radiomics: Images Are More than Pictures, They Are Data
    Gillies, Robert J.
    Kinahan, Paul E.
    Hricak, Hedvig
    [J]. RADIOLOGY, 2016, 278 (02) : 563 - 577
  • [9] Graves A, 2012, STUD COMPUT INTELL, V385, P1, DOI [10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]
  • [10] Deep Residual Learning for Image Recognition
    He, Kaiming
    Zhang, Xiangyu
    Ren, Shaoqing
    Sun, Jian
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 770 - 778
12345