Page 278 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications
P. 278
Chapter 9 Applications of deep learning in biomedical engineering 269
[4] M.I. Razzak, S. Naz, A. Zaib, Deep learning for medical image processing:
overview, challenges and the future, in: Classification in BioApps, Springer,
November 14, 2017.
[5] https://www.datarobot.com/wiki/deep-learning.
[6] C. Cao, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, Z. Xie, Deep
learning and its applications in biomedicine, Genom. Proteom. Bioinf. 16
(February 2018) 17e32.
[7] H. Chen, O. Engkvist, Y. Wang, M. OliveCrona, T. Blaschke, The rise of deep
learning in drug discovery, Drug Discov. Today 23 (June 6, 2018). Elsevier.
[8] https://towardsdatascience.com/a-simple-2d-cnn-for-mnist-digit-
recognition-a998dbc1e79a.
[9] C. Cao, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, Z. Xie, Deep
Learning and its Applications in Biomedicine, July 2017 http://doi.org/10.
1016/j.gpb.2017.07.003.
[10] M.M. Al Rahhal, Y. Bazi, M. Al Zuair, E. Othman, B. BenJdira, Convolutional
neural networks for electrocardiogram classification, J. Med. Biol. Eng.
(March 2018), https://doi.org/10.1007/s40846-018-0389-7.
[11] https://searchenterpriseai.techtarget.com/definition/recurrent-neural-
networks.
[12] H. Al-Askar, N. Radi, A. MacDermott, Recurrent neural networks in medical
data analysis and classifications, Appl. Comp. Med. Health (2016), https://
doi.org/10.1016/B978-0-12-803468-2.00007-2. Elsevier.
[13] J.M. Wolterink, K. Kamnitsas, C. Ledig, I. I sgum, Deep learning: generative
adversarial networks and adversarial methods, Handb. Med. Image
Comput. Comput. Assis. Interv. (2020), https://doi.org/10.1016/B978-0-12-
816176-0.00028-4. Elsevier.
[14] X. Yi, E. Walia, P. Babyn, Generative adversarial network in medical
imaging: a review, Med. Image Anal. 58 (2019), https://doi.org/10.1016/j.
media.2019.101552. Elsevier.
[15] S. Pouyanfar, S. Sadiq, Y. Yan, H. Tian, Y. Tao, M.P. Reyes, M.-L. Shyu, S.-
C. Chen, S.S. Iyengar, A survey on deep learning: algorithms, techniques,
and applications, ACM Comput. Surv. 51 (No. 5) (September 2018).
[16] J. Ker, L. Wang, J. Rao, T. Lim, Deep learning applications in medical image
analysis, IEEE Access (2017), https://doi.org/10.1109/ACCESS.2017.2788044.
[17] A. Kocheturov, P.M. Pardalos, A. Karakitsiou, Massive datasets and machine
learning for computational biomedicine: trends and challenges, Ann. Oper.
Res. 276 (2019) 5e34, https://doi.org/10.1007/s10479-018-2891-2.
[18] Y.-W. Chen, L.C. Jain, Deep Learning in Healthcare Paradigms and
Applications, in: Intelligent Systems Reference Library, vol. 171, Springer,
2020.
[19] S. Michael, M.D. Landau, M.D. LironPantanowitz, Artificial intelligence in
cytopathology: a review of the literature and overview of commercial
landscape, J. Am. Soc. Cytopathol. 8 (Issue 4) (JulyeAugust 2019) 230e241.
Elsevier.
[20] J. Becedas, Brainemachine interfaces: basis and advances 42, IEEE,
November 2012.
[21] K. Kashihara, Automated drug infusion system based on deep convolutional
neural networks, IEEE Int. Conf. Syst. Man Cybern. (2018), https://doi.org/
10.1109/SMC.2018.00286.
[22] Z.-A. Zhu, Y.-C. Lu, C.-H. You, C.-K. Chiang, Deep learning for sensor-based
rehabilitation exercise recognition and evaluation, Sensors (February 20,
2019), https://doi.org/10.3390/s19040887.