Deep learning for image-based cancer detection and diagnosis - A survey

被引：314

作者：

Hu, Zilong ^{[1
]}

Tang, Jinshan ^{[1
,2
,3
]}

Wang, Ziming ^{[2
]}

Zhang, Kai ^{[1
,3
]}

Zhang, Ling ^{[1
]}

Sun, Qingling ^{[4
]}

机构：

[1] Michigan Technol Univ, Sch Technol, Houghton, MI 49931 USA

[2] Michigan Technol Univ, Dept Elect & Comp Engn, Houghton, MI 49931 USA

[3] Wuhan Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan 430065, Hubei, Peoples R China

[4] Sun Technol & Serv LLC, Clinton, MS 39056 USA

来源：

PATTERN RECOGNITION | 2018年 / 83卷

基金：

美国国家卫生研究院;

关键词：

BRAIN-TUMOR SEGMENTATION; CONVOLUTIONAL NEURAL-NETWORK; COMPUTER-AIDED DIAGNOSIS; FALSE-POSITIVE REDUCTION; LUNG NODULE; MITOSIS DETECTION; CLASSIFICATION; ALGORITHMS; DATABASE; MASSES;

D O I：

10.1016/j.patcog.2018.05.014

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we aim to provide a survey on the applications of deep learning for cancer detection and diagnosis and hope to provide an overview of the progress in this field. In the survey, we firstly provide an overview on deep learning and the popular architectures used for cancer detection and diagnosis. Especially we present four popular deep learning architectures, including convolutional neural networks, fully convolutional networks, auto-encoders, and deep belief networks in the survey. Secondly, we provide a survey on the studies exploiting deep learning for cancer detection and diagnosis. The surveys in this part are organized based on the types of cancers. Thirdly, we provide a summary and comments on the recent work on the applications of deep learning to cancer detection and diagnosis and propose some future research directions. (C) 2018 Published by Elsevier Ltd.

引用

页码：134 / 149

页数：16

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