A survey on deep learning in medical image analysis

•A summary of all deep learning algorithms used in medical image analysis is given.•The most successful algorithms for key image analysis tasks are identified.•300 papers applying deep learning to different applications have been summarized. [Display omitted] Deep learning algorithms, in particular...

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Bibliographic Details
Published in:Medical image analysis 2017-12, Vol.42, p.60-88
Main Authors: Litjens, Geert, Kooi, Thijs, Bejnordi, Babak Ehteshami, Setio, Arnaud Arindra Adiyoso, Ciompi, Francesco, Ghafoorian, Mohsen, van der Laak, Jeroen A.W.M., van Ginneken, Bram, Sánchez, Clara I.
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Convolutional neural networks
Deep learning
Diagnostic Imaging
Humans
Image analysis
Image classification
Image detection
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Learning algorithms
Machine Learning
Medical imaging
Neural Networks (Computer)
Object recognition
Retina
Studies
Survey
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Summary:•A summary of all deep learning algorithms used in medical image analysis is given.•The most successful algorithms for key image analysis tasks are identified.•300 papers applying deep learning to different applications have been summarized. [Display omitted] 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.
ISSN:1361-8415
1361-8423
DOI:10.1016/j.media.2017.07.005