Deep Learning in Medical Imagine and Drug Design

Authors

  • Surayya Ado Bala Department of Computer Science and Engineering, Sharda University , India
  • Shri Ojha Kant Research and Technology Development Center, Sharda University, India
  • Adamu Garba Yakasai School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China

DOI:

https://doi.org/10.30564/jhp.v2i2.2683

Abstract

Over the last decade, deep learning (DL) methods have been extremely successful and widely used in almost every domain. Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy. DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data. Medical imaging has transformed healthcare science, it was thought of as a diagnostic tool for disease, but now it is also used in drug design. Advances in medical imaging technology have enabled scientists to detect events at the cellular level. The role of medical imaging in drug design includes identification of likely responders, detection, diagnosis, evaluation, therapy monitoring, and follow-up. A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making. For this, a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment. The result is a quantifiable improvement in healthcare quality in most therapeutic areas, resulting in improvements in quality and life duration. This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design. We briefly discuss the fields related to the history of deep learning, medical imaging, and drug design.

Keywords:

Deep Learning; Medical Imaging; Drugs Design; Cheminformatics

References

[1] Y. Jing, Y. Bian, Z. Hu, L. Wang, X.-Q. S. Xie. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era, The AAPS Journal, 2018, 20: 58.

[2] D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors, Cognitive modeling, 1988, 5: 1.

[3] Y. LeCun, Y. Bengio, G. Hinton. Deep learning, Nature, 2015, 521: 426.

[4] Bala S.A, Kant S., Kumar K. Impact of deep learning in medical imaging: a systematic new proposed model. International Journal of recent technology and engineering, 2019: 112-118.

[5] B. J. Erickson, P. Korfiatis, T. L. Kline, Z. Akkus, K. Philbrick, A. D. Weston. Deep learning in radiology: does one size fit all?, Journal of the American College of Radiology, 2018, 15: 521-526.

[6] X. Glorot, A. Bordes, Y. Bengio. Deep sparse rectifier neural networks, in Proceedings of the fourteenth international conference on artificial intelligence and statistics, 2011: 315-323.

[7] D.-A. Clevert, T. Unterthiner, S. Hochreiter. Fast and accurate deep network learning by exponential linear units (elus), arXiv preprint arXiv:1511.07289, 2015.

[8] D. A. Winkler, T. C. Le. Performance of deep and shallow neural networks, the universal approximation theorem, activity cliffs, and QSAR, Molecular informatics, 2017, 36: 1600118.

[9] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, 2014, 15: 1929-1958.

[10] B. Gao, Y. Xu. Univariant approximation by superpositions of a sigmoidal function, Journal of mathematical analysis and applications, 1993, 178: 221-226.

[11] G. B. Goh, N. O. Hodas,A. Vishnu. Deep learning for computational chemistry, Journal of computational chemistry, 2017, 38: 1291-1307.

[12] S. J. Capuzzi, R. Politi, O. Isayev, S. Farag, A. Tropsha. QSAR modeling of Tox21 challenge stress response and nuclear receptor signaling toxicity assays, Frontiers in Environmental Science, 2016, 4: 3.

[13] K. Simonyan, A. Zisserman. Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv, 2014, 1409.1556.

[14] G. E. Hinton, S. Osindero, Y.-W. Teh. A fast learning algorithm for deep belief nets, Neural computation, 2006, 18: 1527-1554.

[15] A. Ng. Sparse autoencoder, CS294A Lecture notes, 2011, 72: 1-19.

[16] H.-C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, M. O. Leach. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data, IEEE transactions on pattern analysis and machine intelligence, 2013, 35: 1930-1943.

[17] A. Kadurin, A. Aliper, A. Kazennov, P. Mamoshina, Q. Vanhaelen, K. Khrabrov, et al. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology, Oncotarget, 2017, 8: 10883.

[18] P. Domingos, M. Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss, Machine learning, 1997, 29: 103-130.

[19] V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan, B. P. Feuston. Random forest: a classification and regression tool for compound classification and QSAR modeling, Journal of chemical information and computer sciences, 2003, 43: 1947-1958.

[20] C. Cortes, V. Vapnik. Support-vector networks, Machine learning, 1995, 20: 273-297.

[21] S. Ekins. The next era: Deep learning in pharmaceutical research, Pharmaceutical Research, 2016, 33: 2594-2603.

[22] E. Gawehn, J. A. Hiss, G. Schneider. Deep learning in drug discovery, Molecular informatics, 2016, 35: 3-14.

[23] G. Wu, M. Kim, Q. Wang, Y. Gao, S. Liao, D. Shen. Unsupervised deep feature learning for deformable registration of MR brain images, in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013: 649-656.

[24] Bala S.A., Kant S. Deep learning-based model architectures for cardiac MRI segmentation: A Survey. International Journal of Innovative Science, Engineering & Technology, 2020: 129-135.

[25] S. M. Anwar, S. Yousaf, M. Majid. Brain tumor segmentation on Multimodal MRI scans using EMAP Algorithm, in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018: 550-553.

[26] Bala S.A, Kant, S. A hybrid Spatio-temporal network for cardiac MRI segmentation. International Journal of advanced trends in computer science and engineering, 2020: 9089-9097.

[27] P. Moeskops, M. A. Viergever, A. M. Mendrik, L. S. de Vries, M. J. Benders, I. Išgum. Automatic segmentation of MR brain images with a convolutional neural network, IEEE transactions on medical imaging, 2016, 35: 1252-1261.

[28] H. Xue, L. Srinivasan, S. Jiang, M. Rutherford, A. D. Edwards, D. Rueckert, et al. Automatic segmentation and reconstruction of the cortex from neonatal MRI, Neuroimage, 2007, 38: 461-477.

[29] M. Prastawa, J. H. Gilmore, W. Lin, G. Gerig. Automatic segmentation of MR images of the developing newborn brain, Medical image analysis, 2005, 9: 457-466.

[30] W. Zhang, R. Li, H. Deng, L. Wang, W. Lin, S. Ji, et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation, NeuroImage, 2015, 108: 214-224.

[31] Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, O. Ronneberger. 3D U-Net: learning dense volumetric segmentation from sparse annotation, in International conference on medical image computing and computer-assisted intervention, 2016: 424-432.

[32] J. M. Wolterink, T. Leiner, B. D. de Vos, R. W. van Hamersvelt, M. A. Viergever, I. Išgum. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks,Medical image analysis, 2016, 34: 123-136.

[33] Q. Dou, H. Chen, L. Yu, L. Zhao, J. Qin, D. Wang, et al. Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks, IEEE transactions on medical imaging, 2016, 35: 1182-1195.

[34] D. Sheet, S. P. K. Karri, A. Katouzian, N. Navab, A. K. Ray, J. Chatterjee. Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology, in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015: 777-780.

[35] G. Schneider. Adaptive systems in drug design: CRC Press, 2002.

[36] G. E. Hinton, J. A. Anderson. Parallel models of associative memory: updated edition: Psychology press, 2014.

[37] B. F. Begam and J. S. Kumar. A study on cheminformatics and its applications on modern drug discovery, Procedia engineering, 2012, 38: 1264-1275.

[38] F. K. Brown. Chemoinformatics: what is it and how does it impact drug discovery, Annual reports in medicinal chemistry, 1998, 33: 375-384.

[39] V. Arulmozhi, R. Rajesh. Chemoinformatics-a quick review, in 2011 3rd International Conference on Electronics Computer Technology, 2011: 416-419.

[40] R. G. Bone, M. A. Firth, R. A. Sykes. SMILES extensions for pattern matching and molecular transformations: Applications in chemoinformatics, Journal of chemical information and computer sciences, 1999, 39: 846-860.

[41] J. Ma, R. P. Sheridan, A. Liaw, G. E. Dahl, V. Svetnik. Deep neural nets as a method for quantitative structure–activity relationships, Journal of chemical information and modeling, 2015, 55: 263-274.

[42] I. Wallach, M. Dzamba, A. Heifets. AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery, arXiv preprint arXiv, 2015, 1510.02855.

[43] F. Wan, J. Zeng. Deep learning with feature embedding for compound-protein interaction prediction, bioRxiv, 2016: 086033.

[44] A. Mayr, G. Klambauer, T. Unterthiner, S. Hochreiter. DeepTox: toxicity prediction using deep learning, Frontiers in Environmental Science, 2016, 3: 80.

[45] J. Schmidhuber. Deep learning in neural networks: An overview, Neural networks, 2015, 61: 85-117.

[46] H. Chen, O. Engkvist, Y. Wang, M. Olivecrona, T. Blaschke. The rise of deep learning in drug discovery, Drug discovery today, 2018, 23:1241-1250.

[47] Y. Wang, J. Zeng. Predicting drug-target interactions using restricted Boltzmann machines, Bioinformatics, 2013, 29: i126-i134.

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Vol. 2 , Iss. 2 (December 2020)

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