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A survey on deep learning in medical image analysis.

Author information

Affiliations

  • 1. Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
      Authors
    • Litjens G 1
    • Kooi T 1
    • Bejnordi BE 1
    • Setio AAA 1
    • Ciompi F 1
    • Ghafoorian M 1
    • van der Laak JAWM 1
    • van Ginneken B 1
    • Sánchez CI 1
    (9 authors)

ORCIDs linked to this article

Medical Image Analysis, 26 Jul 2017, 42:60-88
https://doi.org/10.1016/j.media.2017.07.005 PMID: 28778026 

Review

Abstract 

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.

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Read article at publisher's site: https://doi.org/10.1016/j.media.2017.07.005

Read article for free, from open access legal sources, via Unpaywall: https://repository.ubn.ru.nl//bitstream/handle/2066/179538/179538.pdfUnpaywall

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Funding 

Funders who supported this work.

Dutch Cancer Society (3)