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Medical Image Enhancement through Deep Learning Methods
In recent years, machine learning algorithms are commonly used in the field of digital image processing for interpreting images based on domain specific knowledge in terms of different aspects like image classification, object/pattern recognition, clinical image diagnosing, traffic control systems, satellite imaging, geomorphological and agriculture sectors etc. to analyse ROI from large amount of captured electronic images via different modalities. Machine Learning (ML) is an outlet of Artificial Intelligence (AI). It has ability to learn by itself without any extra effort like explicit programming. In this paper, we will deliberate the emerged expanse of ML – Deep Learning (DL) which is basically a group of concepts with high level of data abstraction. Its application areas are especially analytical study of medical images such as anatomical structure detection, image registration and enhancement, computer aided disease diagnosis, tissue segmentation, and so on. DL based architecture provides exhilarating results with moral accuracy and enhanced performance for medical image segmentation and classification.
Image Classification, Image Segmentation, Machine Learning, Deep Learning.
- S. Holi, J. Wang and P. Zhao, “LIBOL: A Library for Online Learning Learning Algorithms”, Journal of Machine Learning Research, Vol-15, pp:495-499, 2014.
- R. Smith-Bindman et al., “Use of diagnostic imaging studies and associated radiation exposure for patients enrolled in large integrated health care systems”, JAMA, vol. 307, no. 22, 2012.
- Jusin Ker et al., “Deep Learning Applications in Medical Image Analysis”, DOI: 10.1109/ACCESS.2017.2788044, IEEE Access, 2017.
- Mo. Imran Razzak et al., “Deep Learning for Medical Image Processing: Overview, Challenges and Future”, In Deep Learning for Medical Imaging, 2017.
- Shen et al., “Deep Learning in Medical Image Analysis”, Annual Biomedical Engineering 19:221-248, DOI:10.1146/amurev-bioeng-071516-044442, June 2017.
- Le QV, Ngiam J, Coates A, Lahiri A, Prochnow B, Ng AY., “Optimization methods for deep learning”, Proceedings of International Conference on Machine Learning (ICML), 2011.
- Shin HC, Roth HR, Gao M, Lu L, Xu Z, et al., “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning”, IEEE Transactions on Medical Imaging, 35:1285–1298, [PubMed: 26886976], 2016.
- Zhang W, Li R, Deng H, Wang L, Lin W, et al., “Deep convolutional neural networks for multi-modality isointense infant brain image segmentation”, 108:214–224. [PubMed: 25562829], NeuroImage, 2015.
- M Soltaninejad, “Supervised Learning-based Multimodal MRI Brain Image Analysis”, Ph.D. thesis, University of LINCOLN, 2017.
- Bengio, Y., Courville, A., Vincent, P., “Representation Learning: A Review and New Perspectives”, IEEE Transactions on Pattern Analysis and Machine Intelligence; 35, 1798–1828, doi:10.1109/TPAMI.2013.50, 2013.
- LeCun, Y., Bengio, Y., Hinton, G., “Deep learning. Nature”; 521, 436–444, doi:10.1038/nature14539, 2015.
- El-Gamal, M. Elmogy, and A. Atwan, “Current trends in medical image registration and fusion”, Egyptian Informatic Journal., vol. 17, no. 1, 2016.
- H. R. Roth et al., “Anatomy specific classification of medical images using deep convolutional nets”, in Proceedings, IEEE 12th International Symposium Biomedical Imaging (ISBI), Apr 2015.
- Hinton, G., “A practical guide to training restricted Boltzmann machines”, Momentum 9 (1), pp. 926, 2010.
- Rahul Duggal, Anubha Gupta, Ritu Gupta, Manya Wadhwa, and Chirag Ahuja, “Overlapping cell nuclei segmentation in microscopic images using deep belief networks”, In Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, pp. 82-88, ACM, 2016.
- E. Gibson et al. “A deep-learning platform for medical imaging”, https://arxiv.org/abs/1709.03485, Jan 2017.
- Suk HI, Lee SW, Shen D., “Latent feature representation with stacked auto-encoder for AD/MCI diagnosis”, Brain Structure and Function, 220: 841–859, [PubMed: 24363140], 2015.
- Lee CY, Xie S, Gallagher PW, Zhang Z, Tu Z., “Deeply-supervised nets”, Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), 2015.
- Bengio Y., “Learning deep architectures for AI”, Foundations and Trends in Machine Learning, 2:1–127, 2009.
- Gilbert Lim Yong San, Mong Li Lee, and Wynne Hsu. “Constrainedmser detection of retinal pathology”, In Pattern Recognition (ICPR), 21st International Conference, IEEE, pp. 2059–2062, 2012.
- K. Suzuki, “Overview of deep learning in medical imaging”, Radiol. Phys. Technol., vol. 10, no. 3, 2017.
- G. Litjens et al. “A survey on deep learning in medical image analysis”, https://arxiv.org/abs/ 1702.05747, 2017.
- De Brebisson, A., Montana, G., “Deep neural networks for anatomical brain segmentation”, In: Comput Vis Pattern Recognit, 2015.
- Bar, Y., Diamant, I., Wolf, L., Greenspan, H., “Deep learning with non-medical training used for chest pathology identification”, In: Medical Imaging. Vol. 9414 of Proceedings of the SPIE, 2015.
- D. Shen, G. Wu, and H.-I. Suk, “Deep learning in medical image analysis”, Annual Rev. Biomedicla Engineer, vol. 19, Oct 2017.
- M. J. Cardoso et al., “Deep learning in medical image analysis and multimodal learning for clinical decision support”, in Proc. 3rd Int. Workshop, DLMIA, 7th Int. Workshop, ML-CDS, MICCAI, vol. 10553, Canada, Sep 2017.
- N. Tajbakhsh et al., “Convolutional neural networks for medical image analysis: Full training or fine tuning?” IEEE Transaction, Medical Imaging, vol. 35, no. 5, pp. 1299-1312, May 2016.
- A. Rajkomar, S. Lingam, A. G. Taylor, M. Blum, and J. Mongan, “High throughput classication of radiographs using deep convolutional neural networks”, Journal Digital Imaging, vol. 30, no. 1, pp. 95_101, 2017.
- M. Havaei et al., “Brain tumor segmentation with deep neural networks”, Medical Image Analysis, vol. 35, Jan 2017.
- Dubrovina, A., Kisilev, P., Ginsburg, B., Hashoul, S., Kimmel, R., “Computational mammography using deep neural networks:, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2016.
- Jelmer M Wolterink, Tim Leiner, Max A Viergever, and Ivana Iˇsgum, “Automatic coronary calcium scoring in cardiac ct angiography using convolutional neural networks”, In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 589–596, Springer, 2015.
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