|
參考文獻
[1] Mathur, S., Fuchs, A., Bielicki, J., Van Den Anker, J., & Sharland, M. (2018). Antibiotic use for community-acquired pneumonia in neonates and children: WHO evidence review. Paediatrics and international child health, 38(sup1), S66–S75.
[2] Aydoğdu, M., Ozyilmaz, E., Aksoy, H., Gürsel, G., & Ekim, N. (2010). Mortality prediction in community-acquired pneumonia requiring mechanical ventilation; values of pneumonia and intensive care unit severity scores. Tuberkuloz ve toraks, 58(1), 25–34.
[3] World Health Organization. Pneumonia Vaccine Trial Investigators'
Group & World Health Organization. (2001). Standardization of
interpretation of chest radiographs for the diagnosis of pneumonia in
children / World Health Organization Pneumonia Vaccine Trial
Investigators' Group.
[4] Ruuskanen, O; Lahti, E; Jennings, LC; Murdoch, DR. Viral
pneumonia. (2011). Lancet, 377 (9773): 1264–75.
[5] Hopstaken, R. M., Witbraad, T., van Engelshoven, J. M., & Dinant,
G. J. (2004). Inter-observer variation in the interpretation of chest
radiographs for pneumonia in community-acquired lower respiratory
tract infections. Clinical radiology, 59(8), 743–752.
[6] Mahdieh Poostchi, Kamolrat Silamut, Richard Maude, Stefan
Jaeger, and George Thoma. (2018). Image analysis and machine
learning for detecting malaria. Translational Research, 194, 36-55.
[7] Harry Pratta, Frans Coenenb, Deborah M.Broadbentc, Simon
P.Hardingac, Yalin Zhengac. (2016). Convolutional Neural Networks
for Diabetic Retinopathy. Procedia Computer Science, 90, 200-205.
[8] Y. Fujisawa, Y. Otomo, Y. Ogata, Y. Nakamura, R. Fujita, Y.
Ishitsuka. (2019). Deep-learning-based, computer-aided classifier
developed with a small dataset of clinical images surpasses boardcertified dermatologists in skin tumour diagnosis. Janda and Soyer. Br J Dermatol, 180, 247–248.
[9] Dinggang Shen, Guorong Wu, and Heung-Il Suk. (2017). Deep
Learning in Medical Image Analysis. Biomed, 19, 21–48.
[10]G. Litjens et al. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.
[11]Ravishankar, H., Sudhakar, P., Venkataramani, R., Thiruvenkadam,
S., Annangi, P., Babu, N., & Vaidya, V. (2016). Understanding the
Mechanisms of Deep Transfer Learning for Medical Images. LABELS/DLMIA@MICCAI.
[12]Antin, B., Kravitz, J., & Martayan, E. (2017). Detecting Pneumonia in Chest X-Rays with Supervised Learning.
[13]Paras Lakhani and Baskaran Sundaram. (2017). Deep learning at
chest radiography: automated classification of pulmonary
tuberculosis by using convolutional neural networks. Radiology,
284(2), 574-582.
[14]Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R.M.
(2017). ChestX-Ray8: Hospital-Scale Chest X-Ray Database and
Benchmarks on Weakly-Supervised Classification and Localization
of Common Thorax Diseases. 2017 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 3462-3471.
[15]Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D.Y., Bagul, A., Langlotz, C.P., Shpanskaya, K.S., Lungren, M.P., & Ng, A.Y. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. ArXiv, abs/1711.05225.
[16]D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan and A. Mittal.
(2019). Pneumonia Detection Using CNN based Feature
Extraction. 2019 IEEE International Conference on Electrical,
Computer and Communication Technologies (ICECCT), 1-7.
[17]Glorot, X., Bordes, A. & Bengio, Y. (2011). Deep Sparse Rectifier
Neural Networks. Proceedings of the Fourteenth International
Conference on Artificial Intelligence and Statistics, in PMLR
15:315-323
[18]Ramachandran, P., Zoph, B., & Le, Q.V. (2017). Swish: a Self-Gated Activation Function. arXiv: Neural and Evolutionary Computing.
[19]Zhang, Z., & Sabuncu, M.R. (2018). Generalized Cross Entropy
Loss for Training Deep Neural Networks with Nois Labels. NeurIPS.
[20]Bottou, L., Curtis, F.E., & Nocedal, J. (2018). Optimization Methods for Large-Scale Machine Learning. SIAM Review, 60, 223-311.
[21]Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright. (2011).
Optimization for Machine Learning. The MIT Press.
[22]Kingma, D.P., & Ba, J. (2015). Adam: A Method for Stochastic
Optimization. CoRR, abs/1412.6980.
[23]Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., &
LeCun, Y. (2014). OverFeat: Integrated Recognition, Localization
and Detection using Convolutional Networks. CoRR, abs/1312.6229.
[24]Y. Lecun, L. Bottou, Y. Bengio and P. Haffner. (1998). Gradientbased learning applied to document recognition. in Proceedings of the IEEE, 86(11), 2278-2324
[25]Khan, A., Sohail, A., Zahoora, U., & Qureshi, A.S. (2020). A survey of the recent architectures of deep convolutional neural
networks. Artificial Intelligence Review, 1 - 62.
[26]Krizhevsky, Alex & Sutskever, Ilya & Hinton, Geoffrey. (2012).
ImageNet Classification with Deep Convolutional Neural Networks.
Neural Information Processing Systems. 25. 10.1145/3065386.
[27]Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional
Networks for Large-Scale Image Recognition. CoRR, abs/1409.1556.
[28]Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper
with convolutions. 2015 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 1-9.
[29]Lin, M., Chen, Q., & Yan, S. (2014). Network In Network. CoRR,
abs/1312.4400.
[30]Ioffe, S., & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. ArXiv, abs/1502.03167.
[31]Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z.
(2016). Rethinking the Inception Architecture for Computer
Vision. 2016 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 2818-2826.
[32]S. J. Pan and Q. Yang. (2010). A Survey on Transfer Learning. in
IEEE Transactions on Knowledge and Data Engineering, 22,(10),
1345-1359.
[33]Torrey, Lisa, and Jude Shavlik. (2010). Transfer learning. In
Handbook of research on machine learning applications and trends:
algorithms, methods, and techniques, 242-264.
[34]Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How
transferable are features in deep neural networks? ArXiv,
abs/1411.1792.
[35]Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H.,
Laviolette, F., Marchand, M., & Lempitsky, V.S. (2016). DomainAdversarial Training of Neural Networks. ArXiv, abs/1505.07818.
[36]Johnson, M., Schuster, M., Le, Q.V., Krikun, M., Wu, Y., Chen, Z., Thorat, N., Viégas, F.B., Wattenberg, M., Corrado, G.S., Hughes,
M., & Dean, J. (2017). Google’s Multilingual Neural Machine
Translation System: Enabling Zero-Shot Translation. Transactions of
the Association for Computational Linguistics, 5, 339-351.
[37]Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C., Liang, H.,
Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., Dong, J.,
Prasadha, M. K., Pei, J., Ting, M., Zhu, J., Li, C., Hewett, S., Dong, J., Ziyar, I., Shi, A., … Zhang, K. (2018). Identifying Medical
Diagnoses and Treatable Diseases by Image-Based Deep
Learning. Cell, 172(5), 1122–1131.e9.
[38]Shorten, C., & Khoshgoftaar, T.M. (2019). A survey on Image Data
Augmentation for Deep Learning. Journal of Big Data, 6, 1-48.
[39]Tom Fawcett. (2006). An introduction to ROC analysis. Pattern
Recogn. Lett. 27, 8, 861–874.
[40]Bradley, Andrew P. (1997) The use of the area under the ROC curve
in the evaluation of machine learning algorithms. Pattern
Recognition, 30(7), 1145-1159. |