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Deep learning aided decision support for pulmonary nodules diagnosing: a review
Yang, Yixin1,2; Feng, Xiaoyi1,2; Chi, Wenhao1,2; Li, Zhengyang1,2; Duan, Wenzhe1,2; Liu, Haiping3; Liang, Wenhua4; Wang, Wei4; Chen, Ping3; He, Jianxing4; Liu, Bo1
2018-04-01
Source PublicationJOURNAL OF THORACIC DISEASE
ISSN2072-1439
Volume10Pages:S867-S875
AbstractDeep learning techniques have recently emerged as promising decision supporting approaches to automatically analyze medical images for different clinical diagnosing purposes. Diagnosing of pulmonary nodules by using computer-assisted diagnosing has received considerable theoretical, computational, and empirical research work, and considerable methods have been developed for detection and classification of pulmonary nodules on different formats of images including chest radiographs, computed tomography (CT), and positron emission tomography in the past five decades. The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction, and benign-malignant classification for the huge volume of chest scan data. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the deep learning aided decision support for pulmonary nodules diagnosing. As far as the authors know, this is the first time that a review is devoted exclusively to deep learning techniques for pulmonary nodules diagnosing.
KeywordComputer-aided diagnosis convolutional neural network (CNN) deep learning pulmonary nodules
DOI10.21037/jtd.2018.02.57
Language英语
Funding ProjectKey Research Program of Frontier Sciences, Chinese Academy of Sciences[QYZDB-SSW-SYS020]
WOS Research AreaRespiratory System
WOS SubjectRespiratory System
WOS IDWOS:000431684000012
PublisherAME PUBL CO
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/30242
Collection系统科学研究所
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Guangzhou Med Univ, Affiliated Hosp 1, PET CT Ctr, Guangzhou 510120, Guangdong, Peoples R China
4.Guangzhou Med Univ, Affiliated Hosp 1, Dept Thorac Surg & Oncol, Guangzhou 510120, Guangdong, Peoples R China
Recommended Citation
GB/T 7714
Yang, Yixin,Feng, Xiaoyi,Chi, Wenhao,et al. Deep learning aided decision support for pulmonary nodules diagnosing: a review[J]. JOURNAL OF THORACIC DISEASE,2018,10:S867-S875.
APA Yang, Yixin.,Feng, Xiaoyi.,Chi, Wenhao.,Li, Zhengyang.,Duan, Wenzhe.,...&Liu, Bo.(2018).Deep learning aided decision support for pulmonary nodules diagnosing: a review.JOURNAL OF THORACIC DISEASE,10,S867-S875.
MLA Yang, Yixin,et al."Deep learning aided decision support for pulmonary nodules diagnosing: a review".JOURNAL OF THORACIC DISEASE 10(2018):S867-S875.
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