Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Med Image Anal. 2017 Dec;42:60-88. doi: 10.1016/j.media.2017.07.005. Epub 2017 Jul 26.
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.
深度学习算法，特别是卷积网络，已经迅速成为医学图像分析的首选方法。本文综述了与医学图像分析相关的主要深度学习概念，并总结了 300 多篇相关领域的研究论文，其中大多数发表于去年。我们调查了深度学习在图像分类、目标检测、分割、配准等任务中的应用。我们按应用领域（神经、视网膜、肺部、数字病理学、乳腺、心脏、腹部、肌肉骨骼）提供了每项研究的简明概述。最后，我们总结了当前的最新技术，对开放挑战进行了批判性讨论，并为未来的研究方向提出了建议。
