Page 66 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

54 Chapter 2 Deep convolutional neural network in medical image processing

[32] Y. Kobayashi, H. Kobayashi, J.T. Giles, I. Yokoe, M. Hirano, Y. Nakajima,
M. Takei, Detection of left ventricular regional dysfunction and
myocardial abnormalities using complementary cardiac magnetic
resonance imaging in patients with systemic sclerosis without cardiac
symptoms: a pilot study, Intern. Med. 55 (3) (2016) 237e243.
[33] I. Cabria, I. Gondra, MRI segmentation fusion for brain tumor detection,
Inf. Fusion 36 (2017) 1e9.
[34] G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian,
J.A. Van Der Laak, B. Van Ginneken, C.I. S anchez, A survey on deep
learning in medical image analysis, Med. Image Anal. 42 (2017) 60e88.
[35] X. Cheng, L. Zhang, Y. Zheng, Deep similarity learning for multimodal
medical images, Comput. Methods Biomech. Biomed. Eng.: Imaging Vis. 6
(3) (2018) 248e252.
[36] M. Simonovsky, B. Guti errez-Becker, D. Mateus, N. Navab, N. Komodakis,
October. A deep metric for multimodal registration, in: International
Conference on Medical Image Computing and Computer-Assisted
Intervention, Springer, Cham, 2016, pp. 10e18.
[37] S. Miao, Z.J. Wang, R. Liao, A CNN regression approach for real-time 2D/
3D registration, IEEE Trans. Med. Imaging 35 (5) (2016) 1352e1363.
[38] N. Tajbakhsh, J.Y. Shin, S.R. Gurudu, R.T. Hurst, C.B. Kendall,
M.B. Gotway, J. Liang, Convolutional neural networks for medical image
analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35 (5)
(2016) 1299e1312.
[39] D.H. Hubel, T.N. Wiesel, Receptive fields of single neurones in the cat's
striate cortex, J. Physiol. 148 (3) (1959) 574e591.
[40] S.M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, M.K. Khan,
Medical image analysis using convolutional neural networks: a review,
J. Med. Syst. 42 (11) (2018) 226.
[41] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning
applied to document recognition, Proc. IEEE 86 (11) (1998) 2278e2324.
[42] A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep
convolutional neural networks, in: Advances in Neural Information
Processing Systems, 2012, pp. 1097e1105.
[43] K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-
Scale Image Recognition, 2014 arXiv preprint arXiv:1409.1556.
[44] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan,
V. Vanhoucke, A. Rabinovich, Going Deeper with Convolutions, 2014,
p. 1409, arXiv 2014. arXiv preprint arXiv:1409.4842.
[45] M. Lin, Q. Chen, S. Yan, Network in Network, 2013 arXiv preprint arXiv:
1312.4400.
[46] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image
recognition, in: Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition, 2016, pp. 770e778.
[47] V. Gulshan, L. Peng, M. Coram, M.C. Stumpe, D. Wu, A. Narayanaswamy,
S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, Development
and validation of a deep learning algorithm for detection of diabetic
retinopathy in retinal fundus photographs, Jama 316 (22) (2016)
2402e2410.
[48] A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, S. Thrun,
Dermatologist-level classification of skin cancer with deep neural
networks, Nature 542 (7639) (2017) 115e118.