[1] Wikipedia. Pneumonia[EB/OL].[2018-12-06].https://en.wikipedia.org/wiki/Pneumonia. [2] 世界卫生组织. 肺炎[EB/OL].[2016-11-07].https://www.who.int/zh/news-room/fact-sheets/detail/pneumonia.(World Health Organization. Pneumonia[EB/OL].[2016-11-07].https://www.who.int/zh/news-room/fact-sheets/detail/pneumonia.) [3] 冯江,袁秀琴,朱军,等.中国2000-2010年5岁以下儿童死亡率和死亡原因分析[J].中华流行病学杂志,2012,33(6):558-561.(FENG J, YUAN X Q, ZHU J, et al. Under-5-mortality rate and causes of death in China, 2000 to 2010[J]. Chinese Journal of Epidemiology, 2012, 33(6):558-561.) [4] 李方,方征.肺炎的分类[J].中国乡村医生,1993(11):16-17.(LI F, FANG Z. Classification of pneumonia[J]. Chinese Country Doctor, 1993(11):16-17.) [5] 朱迎钢,瞿介明.老年人重症肺炎的难点和临床对策[J].中华老年医学杂志,2008,27(1):1-4.(ZHU Y G, QU J M. Difficulty and clinical strategy on severe pneumonia in the elderly[J]. Chinese Journal of Geriatrics, 2008, 27(1):1-4.) [6] JOHRI A. CT based semi-automated method for pneumonia severity in mice[J]. Journal of Young Investigators, 2010, 19(21):2-8. [7] SHIRAISHI J, LI Q, APPELBAUM D, et al. Computer-aided diagnosis and artificial intelligence in clinical imaging[J]. Seminars in Nuclear Medicine, 2011, 41(6):449-462. [8] 岳路,马凌燕,魏本征.基于决策树算法的小儿肺炎临床辨证分类模型研究[J].电子测试,2013(5):243-244.(YUE L, MA L Y, WEI B Z. Research on children pneumonia clinical syndrome classification model based on decision tree algorithm[J]. Electronic Test, 2013(5):243-244.) [9] QUINLAN J R. Induction of decision trees[J]. Machine Learning, 1986, 1(1):81-106. [10] JUN S, PARK B, SEO J B, et al. Development of a computer-aided differential diagnosis system to distinguish between usual interstitial pneumonia and non-specific interstitial pneumonia using texture- and shape-based hierarchical classifiers on HRCT images[J]. Journal of Digital Imaging, 2018, 31(2):235-244. [11] HEARST M A, DUMAIS S T, OSUNA E, et al. Support vector machines[J]. IEEE Intelligent Systems and their Applications, 1998, 13(4):18-28. [12] 邵欣蔚.基于SVM+算法的儿童社区获得性肺炎早期诊断研究[D].上海:华东师范大学,2017:54-79.(SHAO X W. Early diagnosis of community acquired pneumonia in children based on SVM+ algorithm[D]. Shanghai:East China Normal University, 2017:54-79.) [13] GOODFELLOW I, BENGIO Y, COURVILLE A. Deep Learning[M]. Cambridge:MIT Press, 2016:11-29. [14] HAYKIN S, KOSKO B. GradientBased Learning Applied to Document Recognition[M]. Piscataway:Wiley-IEEE Press, 2001:78-83. [15] 刘长征,相文波.基于改进卷积神经网络的肺炎影像判别[J].计算机测量与控制,2017,25(4):185-188.(LIU C Z, XIANG W B. Recognition of pneumonia type based on improved convolution neural network[J]. Computer Measurement and Control, 2017, 25(4):185-188.) [16] RAJPURKAR P, IRVIN J, ZHU K, et al. CheXNet:radiologist-level pneumonia detection on chest X-rays with deep learning[EB/OL].[2018-12-25]. https://arxiv.org/pdf/1711.05225.pdf. [17] HINTON G, VINYALS O, DEAN J. Distilling the knowledge in a neural network[EB/OL].[2015-03-09]. https://arxiv.org/pdf/1503.02531.pdf. [18] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 2012 International Conference on Neural Information Processing Systems. New York:Curran Associates Inc., 2012:1097-1105. [19] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL].[2019-03-10]. https://arxiv.org/pdf/1409.1556.pdf. [20] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2016:770-778. [21] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2016:2818-2826. [22] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2016:1-9. [23] KERMANY D S, GOLDBAUM M, CAI W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 2018, 172(5):1122-1131. [24] PAN S J, YANG Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10):1345-1359. |