A survey on deep learning in medical image analysis
A survey on deep learning in medical image analysis
复制标题
DOI：
10.1016/j.media.2017.07.005
复制
发表时间：
2017-12-01
影响因子：
10.9
通讯作者：
Sanchez, Clara I.
中科院分区：
文献类型：
--
作者：
Litjens, Geert;Kooi, Thijs;Sanchez, Clara I.
深度学习算法，特别是卷积网络，已迅速成为分析医学图像的首选方法。本文回顾了与医学图像分析相关的主要深度学习概念，并总结了该领域的300多项贡献，其中大部分出现在去年。我们调查了深度学习在图像分类、对象检测、分割、配准和其他任务中的应用。简要概述了每个应用领域的研究：神经，视网膜，肺，数字病理学，乳腺，心脏，腹部，肌肉骨骼。最后，我们总结了目前的最先进的，开放的挑战和未来研究方向的关键讨论。(C)2017爱思唯尔B.V.保留所有权利。
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. (C) 2017 Elsevier B.V. All rights reserved.