Machine Learning in Medical Imaging

被引:335
作者
Giger, Maryellen L. [1 ]
机构
[1] Univ Chicago, Dept Radiol, MC 2026,5841 S Maryland Ave, Chicago, IL 60637 USA
关键词
Machine learning; deep learning; radiomics; computer-aided diagnosis; computer-assisted decision support; VOLUMETRIC BREAST DENSITY; COMPUTER-AIDED DETECTION; NEURAL-NETWORK; LESIONS; RISK; CLASSIFICATION; RADIOMICS; IMAGES; ENHANCEMENT; VALIDATION;
D O I
10.1016/j.jacr.2017.12.028
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
Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data. The ultimate benefit of quantitative radiomics is to (1) yield predictive image-based phenotypes of disease for precision medicine or (2) yield quantitative image-based phenotypes for data mining with other-omics for discovery (ie, imaging genomics). For deep learning in radiology to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly, and subtle differences in disease states are more difficult to perceive than differences in everyday objects. In the future, machine learning in radiology is expected to have a substantial clinical impact with imaging examinations being routinely obtained in clinical practice, providing an opportunity to improve decision support in medical image interpretation. The term of note is decision support, indicating that computers will augment human decision making, making it more effective and efficient. The clinical impact of having computers in the routine clinical practice may allow radiologists to further integrate their knowledge with their clinical colleagues in other medical specialties and allow for precision medicine.
引用
收藏
页码:512 / 520
页数:9
相关论文
共 56 条
  • [1] Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
    Aerts, Hugo J. W. L.
    Velazquez, Emmanuel Rios
    Leijenaar, Ralph T. H.
    Parmar, Chintan
    Grossmann, Patrick
    Cavalho, Sara
    Bussink, Johan
    Monshouwer, Rene
    Haibe-Kains, Benjamin
    Rietveld, Derek
    Hoebers, Frank
    Rietbergen, Michelle M.
    Leemans, C. Rene
    Dekker, Andre
    Quackenbush, John
    Gillies, Robert J.
    Lambin, Philippe
    [J]. NATURE COMMUNICATIONS, 2014, 5
  • [2] Validation of a method for measuring the volumetric breast density from digital mammograms
    Alonzo-Proulx, O.
    Packard, N.
    Boone, J. M.
    Al-Mayah, A.
    Brock, K. K.
    Shen, S. Z.
    Yaffe, M. J.
    [J]. PHYSICS IN MEDICINE AND BIOLOGY, 2010, 55 (11) : 3027 - 3044
  • [3] [Anonymous], QUANT IM BIOM ALL QI
  • [4] [Anonymous], 2013, 31 INT C MACH LEARN
  • [5] [Anonymous], SEMIN BREAST DIS
  • [6] [Anonymous], INVEST RADIOL
  • [7] [Anonymous], ABS14111792 CORR
  • [8] [Anonymous], QUANT IM NETW QIN
  • [9] Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
    Anthimopoulos, Marios
    Christodoulidis, Stergios
    Ebner, Lukas
    Christe, Andreas
    Mougiakakou, Stavroula
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2016, 35 (05) : 1207 - 1216
  • [10] A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets
    Antropova, Natalia
    Huynh, Benjamin Q.
    Giger, Maryellen L.
    [J]. MEDICAL PHYSICS, 2017, 44 (10) : 5162 - 5171