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 and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.
Description
[1702.05747] A Survey on Deep Learning in Medical Image Analysis
cite arxiv:1702.05747Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 2017
%0 Generic
%1 litjens2017survey
%A Litjens, Geert
%A Kooi, Thijs
%A Bejnordi, Babak Ehteshami
%A Setio, Arnaud Arindra Adiyoso
%A Ciompi, Francesco
%A Ghafoorian, Mohsen
%A van der Laak, Jeroen A. W. M.
%A van Ginneken, Bram
%A Sánchez, Clara I.
%D 2017
%K DNN medical review
%R 10.1016/j.media.2017.07.005
%T A Survey on Deep Learning in Medical Image Analysis
%U http://arxiv.org/abs/1702.05747
%X 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 and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.
@misc{litjens2017survey,
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 and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.},
added-at = {2018-09-13T21:33:42.000+0200},
author = {Litjens, Geert and Kooi, Thijs and Bejnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and van der Laak, Jeroen A. W. M. and van Ginneken, Bram and Sánchez, Clara I.},
biburl = {https://www.bibsonomy.org/bibtex/293cef160b2f09d59b2b5089be00d714e/huntstart},
description = {[1702.05747] A Survey on Deep Learning in Medical Image Analysis},
doi = {10.1016/j.media.2017.07.005},
interhash = {964b9b21bbe67671ac9ecf7ba6c7bc7f},
intrahash = {93cef160b2f09d59b2b5089be00d714e},
keywords = {DNN medical review},
note = {cite arxiv:1702.05747Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 2017},
timestamp = {2018-09-18T18:52:17.000+0200},
title = {A Survey on Deep Learning in Medical Image Analysis},
url = {http://arxiv.org/abs/1702.05747},
year = 2017
}