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Med Image Anal. 2017 Dec;42:60-88. doi: 10.1016/j.media.2017.07.005. Epub 2017 Jul 26.

A survey on deep learning in medical image analysis.

Author information

1
Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: geert.litjens@radboudumc.nl.
2
Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.

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.

KEYWORDS:

Convolutional neural networks; Deep learning; Medical imaging; Survey

PMID:
28778026
DOI:
10.1016/j.media.2017.07.005
[Indexed for MEDLINE]

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