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Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect

文献类型：期刊论文

作者Liu, Bo2; Chi, Wenhao2,6; Li, Xinran5; Li, Peng2; Liang, Wenhua1,4; Liu, Haiping3,4; Wang, Wei1,4; He, Jianxing1,4
刊名JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
出版日期2019-11-30
页码33
关键词Computer-aided diagnosis Pulmonary nodules Lung cancer Deep learning Artificial intelligence Review
ISSN号0171-5216
DOI10.1007/s00432-019-03098-5
英文摘要Purpose Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret the massive volume of chest images generated daily, computer-assisted diagnosis of pulmonary nodule has opened up new opportunities to relax the limitation from physicians' subjectivity, experiences and fatigue. And the fair access to the reliable and affordable computer-assisted diagnosis will fight the inequalities in incidence and mortality between populations. It has been witnessed that significant and remarkable advances have been achieved since the 1980s, and consistent endeavors have been exerted to deal with the grand challenges on how to accurately detect the pulmonary nodules with high sensitivity at low false-positive rate as well as on how to precisely differentiate between benign and malignant nodules. There is a lack of comprehensive examination of the techniques' development which is evolving the pulmonary nodules diagnosis from classical approaches to machine learning-assisted decision support. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the computer-assisted nodules detection and benign-malignant classification techniques developed over three decades, which have evolved from the complicated ad hoc analysis pipeline of conventional approaches to the simplified seamlessly integrated deep learning techniques. This review also identifies challenges and highlights opportunities for future work in learning models, learning algorithms and enhancement schemes for bridging current state to future prospect and satisfying future demand. Conclusion It is the first literature review of the past 30 years' development in computer-assisted diagnosis of lung nodules. The challenges indentified and the research opportunities highlighted in this survey are significant for bridging current state to future prospect and satisfying future demand. The values of multifaceted driving forces and multidisciplinary researches are acknowledged that will make the computer-assisted diagnosis of pulmonary nodules enter into the main stream of clinical medicine and raise the state-of-the-art clinical applications as well as increase both welfares of physicians and patients. We firmly hold the vision that fair access to the reliable, faithful, and affordable computer-assisted diagnosis for early cancer diagnosis would fight the inequalities in incidence and mortality between populations, and save more lives.
资助项目Key Research Program of Frontier Sciences, Chinese Academy of Sciences[QYZDB-SSW-SYS020] ; Major Project to Promote Development of Big Data from National Development and Reform Commission[2016-999999-65-01-000696-01] ; Collaboration Research Project of Guangdong Education Department[GJHZ1006] ; Collaboration Research Project of Guangdong Education Department[2014KGJHZ010] ; Medical and Health Science and Technology Project of Guangzhou Municipal Health Commission[20161A011060] ; Science and Technology Planning Project of Guangdong Province[2017A020215110] ; Natural Science Foundation of Guangdong Province[2018A030313534]
WOS研究方向Oncology
语种英语
WOS记录号WOS:000499571500003
出版者SPRINGER
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/50377]  
专题中国科学院数学与系统科学研究院
通讯作者Liu, Bo; He, Jianxing
作者单位1.Guangzhou Med Univ, Affiliated Hosp 1, Dept Thorac Surg & Oncol, Guangzhou, Guangdong, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
3.Guangzhou Med Univ, Affiliated Hosp 1, PET CT Ctr, Guangzhou, Guangdong, Peoples R China
4.China State Key Lab Resp Dis, Guangzhou, Guangdong, Peoples R China
5.Univ Wisconsin, Dept Math, Madison, WI 53706 USA
6.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Bo,Chi, Wenhao,Li, Xinran,et al. Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect[J]. JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY,2019:33.
APA Liu, Bo.,Chi, Wenhao.,Li, Xinran.,Li, Peng.,Liang, Wenhua.,...&He, Jianxing.(2019).Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect.JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY,33.
MLA Liu, Bo,et al."Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect".JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY (2019):33.

入库方式： OAI收割

来源：数学与系统科学研究院

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