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The Transactions of
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Trans. Korean. Inst. Elect. Eng.
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2021-07
(Vol.70 No.7)
10.5370/KIEE.2021.70.7.1036
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1
D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, 2018, Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell, Vol. 172, No. 5, pp. 1122-1131
2
J. Kugelman, D. Alonso-Caneiro, S. A. Read, J. Hamwood, S. J. Vincent, F. K. Chen, M. J. Collins, 2019, Automatic choroidal segmentation in oct images using supervised deep learning methods, Scientific reports, Vol. 9, No. 1, pp. 1-13
3
J. Wang, R. Ju, Y. Chen, L. Zhang, J. Hu, Y. Wu, W. Dong, J. Zhong, Z. Yi, 2018, Automated retinopathy of prematurity screening using deep neural networks, EBioMedicine, Vol. 35, pp. 361-368
4
X. Li, T. Pang, B. Xiong, W. Liu, P. Liang, T. Wang, 2017, Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification, in 2017 10th international congress on image and signal processing biomedical engineering and informatics (CISP-BMEI), pp. 1-11
5
C. S. Lee, D. M. Baughman, A. Y. Lee, 2017, Deep learning is effective for classifying normal versus age-related macular degeneration oct images, Ophthalmology Retina, Vol. 1, No. 4, pp. 322-327
6
S. Kaymak, A. Serener, 2018, Automated age-related macular degeneration and diabetic macular edema detection on oct images using deep learning, in 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 265-269
7
E. Strickland, 2019, Ibm watson, heal thyself: How ibm overpromised and underdelivered on ai health care, IEEE Spectrum, Vol. 56, No. 4, pp. 24-31
8
G. Huang, Z. Liu, L. Van Der Maaten, K. Q. Weinberger, 2017, Densely connected convolutional networks, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708
9
B. Tan, A. Wong, K. Bizheva, 2018, Enhancement of morphological and vascular features in oct images using a modified bayesian residual transform, Biomedical optics express, Vol. 9, No. 5, pp. 2394-2406
10
D. P. Kingma, J. Ba, 2014, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980
11
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, 2014, Dropout: a simple way to prevent neural networks from overfitting, The journal of machine learning research, Vol. 15, No. 1, pp. 1929-1958
12
M. Schuster, K. Paliwal, 1997, Bidirectional recurrent neural networks, IEEE Transactions on Signal Processing, Vol. 45, No. 11, pp. 2673-2681
13
P. Y. Simard, D. Steinkraus, J. C. Platt, 2003, Best practices for convolutional neural networks applied to visual document analysis, in Icdar, Vol. 3