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.
   273   274   275   276   277   278   279   280   281   282   283