Page 299 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications
P. 299
290 Chapter 10 Deep neural network in medical image processing
[8] K. Itoh, ID number recognition of X-ray films by a neural network, Comput.
Methods Progr. Biomed. 43 (1e2) (1994) 15e18, https://doi.org/10.1016/
0169-2607(94)90179-1.
[9] Z. Bai, L.L.C. Kasun, G.B. Huang, Generic object recognition with local
receptive fields based extreme learning machine, Proc. Comp. Sci. 53 (1)
(2015) 391e399, https://doi.org/10.1016/j.procs.2015.07.316.
[10] A. Joly, H. Goëau, P. Bonnet, V. Baki c, J. Barbe, S. Selmi, et al., Interactive
plant identification based on social image data, Ecol. Inf. 23 (2014) 22e34,
https://doi.org/10.1016/j.ecoinf.2013.07.006.
[11] F. Murat, O. Yildirim, M. Talo, U.B. Baloglu, Y. Demir, U.R. Acharya,
Application of deep learning techniques for heartbeats detection using ECG
signals-analysis and review, Comput. Biol. Med. 120 (February 2020)
103726, https://doi.org/10.1016/j.compbiomed.2020.103726.
[12] S. Hao, Y. Zhou, Y. Guo, A brief survey on semantic segmentation with deep
learning, Neurocomputing (2020), https://doi.org/10.1016/j.neucom.2019.11.118.
[13] P. Gopika, V. Sowmya, E. Gopalakrishnan, K. Soman, Transferable Approach
for Cardiac Disease Classification Using Deep Learning, Elsevier Inc., 2020,
https://doi.org/10.1016/b978-0-12-819061-6.00012-4.
[14] N. Navab, J. Hornegger, W.M. Wells, A.F. Frangi, in: Medical Image
Computing and Computer-assisted Intervention e MICCAI 2015: 18th
International Conference Munich, Germany, October 5e9, 2015
Proceedings, Part III, Lecture Notes in Computer Science (Including
Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics) 9351 (Cvd), 2015, pp. 12e20, https://doi.org/10.1007/978-3-
319-24574-4.
[15] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., Going
deeper with convolutions, in: 2015 IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), vol. 91, IEEE, 2015, pp. 1e9, https://
doi.org/10.1109/CVPR.2015.7298594, http://doi.wiley.com/10.1002/jctb.
4820. U, http://ieeexplore.ieee.org/document/7298594/.
[16] Y. Lecun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (7553) (2015)
436e444, https://doi.org/10.1038/nature14539.
[17] X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, Y. Fan, A deep learning model
integrating FCNNs and CRFs for brain tumor segmentation, Med. Image
Anal. 43 (2018) 98e111, https://doi.org/10.1016/j.media.2017.10.002.
[18] H. Zhang, Y. Liu, B. Xie, J. Yu, Orientation contrast model for boundary
detection, J. Vis. Commun. Image Represent. 25 (5) (2014) 774e784, https://
doi.org/10.1016/j.jvcir.2014.01.011.
[19] M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, et al.,
Brain tumor segmentation with deep neural networks, Med. Image Anal. 35
(2017) 18e31, https://doi.org/10.1016/j.media.2016.05.004, arXiv:1505.03540.
[20] A. Bria, C. Marrocco, F. Tortorella, Addressing class imbalance in deep learning
for small lesion detection on medical images, Comput. Biol. Med. 120
(February) (2020) 103735, https://doi.org/10.1016/j.compbiomed.2020.103735.
[21] L. Saba, M. Biswas, V. Kuppili, E. Cuadrado Godia, H.S. Suri, D.R. Edla, et
al., The present and future of deep learning in radiology, Eur. J. Radiol. 114
(February 2019) 14e24, https://doi.org/10.1016/j.ejrad.2019.02.038.
[22] Z. Liu, C. Yao, H. Yu, T. Wu, Deep reinforcement learning with its
application for lung cancer detection in medical Internet of Things, Future
Generat. Comput. Syst. 97 (2019) 1e9, https://doi.org/10.1016/
j.future.2019.02.068.