DERİN ÖĞRENME VE SAĞLIK ALANINDAKİ UYGULAMALARI

Yapay Zekâ (YZ) tıp dünyasında yeni dönüşümlere neden oluyor. Bugün YZ, doktorların daha hızlı, daha doğru teşhis yapmalarına yardım edebilir, hastalıkları önceden önlemek için hastalık riskini tahmin edebilir ve araştırmacıların, genetik varyasyonların hastalığa nasıl yol açtığını anlamalarına yardımcı olabilir. YZ yıllardır var olmasına rağmen son yıllarda yeni bir öğrenme şekli olan “Derin Öğrenme-Deep Learning (DL)” ile adeta bir patlama yarattı. Bu yeni YZ tekniği sayesinde kendi kendini süren araçlar, süper-insan görüntü tanıma ve tıpta yaşamı değiştiren, hatta hayat kurtaran gelişmeler mümkün hale geldi. DL, araştırmacıların hastalık tedavisi için tıbbi verileri analiz etmelerine yardımcı olmakta, doktorların tıbbi görüntüleri analiz etme yeteneğini artırmakta ve kişiselleştirilmiş ilacın geleceğine önemli katkı sağlamaktadır. Temel olarak üç eğilim DL devrimini yönlendirmektedir. Bunlar daha güçlü GPU’lar (Grafik İşlem Birimi), insan beynini modelleyen gelişmiş sinir ağı algoritmaları ve internetten büyü verilere erişim. Bu çalışma YZ teknolojilerinden biri olan makine öğrenme alanında yeni gelişen derin öğrenme konusuna odaklanmıştır. Derin öğrenmenin ne olduğu, önemi, en bilinen DL mimarileri üzerinde durur. Derin öğrenmenin hayatımızda pek çok şeyi değiştirmek ve geliştirmek üzere ciddi potansiyeli vardır ve bunun üzerine çok ciddi yatırımlar yapılarak araştırmalar her boyutta desteklenmektedir. Ancak insan hayatına dokunan sağlık alandaki potansiyeli ve önemi etkili gelişmelere yol açmaktadır. Bu çalışma, sağlık alanındaki uygulamaları ve değerli bilim insanlarının bu alanda yaptığı uluslararası çalışmaları ortaya koymaktadır. Sonuç olarak böylesine büyük bir potansiyele sahip derin öğrenme teknolojisinin bizim ülkemizdeki sağlık uygulamalarına katacak çok şeyi vardır. Bu alanda dünyada olup bitenler yakından takip edilmelidir. Değerli araştırmacılarımız tarafından devletin desteği ve sağlık kurumlarının işbirliği ile insan hayatını değiştiren ve koruyan sağlık uygulamaları geliştirilebilir ve bu uygulamalar sağlık sistemine başarılı bir şekilde entegre edilebilir. Bu bağlamda ortaya koyulan bu araştırma makalesinin alana önemli katkı sağlayacağı öngörülmektedir.

DEEP LEARNING AND APPLICATIONS IN HEALTH

Artificial Intelligence (AI) causes new transformations in the medical world. Today, AI can help doctors diagnose faster, more accurately, predict disease risk to prevent diseases beforehand, and help researchers understand how genetic variations lead to disease. Although AI has existed for years, it has created an explosion in recent years with Deep Learning (DL) which is a new form of learning. Thanks to this new AI technique, self-driving tools, super-human image recognition and life-changing, even life-saving developments have become possible. The DL helps researchers analyze medical data for disease treatment, increases physicians' ability to analyze medical images, and contributes significantly to the future of the personalized drug. Basically three trends lead the DL revolution. These are more powerful GPUs (Graphics Processing Unit), advanced neural network algorithms that model the human brain, and access data from the Internet. This study focuses on the emerging deep learning in the field of machine learning, one of the AI technologies. What deep learning is, and its importance, emphasizes the most well-known DL architectures. Deep learning has serious potential to change and improve many things in our lives, and researches are supported in every aspect by making serious investments. However, it’s potential and importance in the field of health touching human life leads to great improvements. This study demonstrates the practices in the field of health and international studies of valuable scientists in this field. As a result, deep learning technology with such a great potential has much to add to the health practices in our country. What is happening in the world in this field should be followed closely. Health practices that change and protect human life can be developed by our valuable researchers with the support of the state and the cooperation of health institutions and these applications can be successfully integrated into the health system. It is foreseen that this research paper presented in this context will contribute significantly to the field.

PDF

___

Anthimopoulos M., Christodoulidis S., Ebner L., Christe A., Mougiakakou S. (2016). Lung pattern classification for interstitial lung diseases using a deep convolutional neural network, IEEE Trans. Med. Imaging, 35(5), 1207-1216.
Bashivan P., Rish I., Yeasin M., Codella N.(2016). Learning representations from EEG with deep recurrent-convolutional neural networks, ArXiv e-prints, 1-15.
Bengio, Y., Lamblin P., Popovici D., & Larochelle H. (2006). Greedy layer-wise training of deep networks, Proceedings of the 19th International Conference on Neural Information Processing Systems. MIT Press, 153-160.
Christodoulidis S., Anthimopoulos M., Ebner L., Christe A., Mougiakakou S. (2017). Multisource transfer learning with convolutional neural networks for lung pattern analysis, IEEE J. Biomed. Health Inf. 21(1), 76-84.
Elman J.L. (1990). Finding Structure in Time, Cogn. Sci., 14(2), 179-211.
Esteva A., Kuprel B., Novoa R.A., et al. (2017). Dermatologist-level classification of skin Cancer with deep neural networks. Nature, 542,115-8.
Delen D., Walker G., Kadam A. (2005). Predicting breast cancer survivability: a com- parison of three data mining methods, Artif. Intell. Med., 34(2), 113-127.
Donoghue J.O., Roantree M., Boxtel M.V. (2015). A configurable deep network for high- dimensional clinical trial data, IEEE International Joint Conference on Neural Networks (IJCNN),1-8.
Liang Z., Zhang G., Huang J.X., Hu Q.V. (2014). Deep learning for healthcare decision making with emrs, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE Computer Society, Los Alamitos, CA, USA, 556–559.
Pham T., Tran T., Phung D., Venkatesh S. (2016). Deepcare: a deep dynamic memory model for predictive medicine, Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference (PAKDD), Springer International Publishing, Cham, 30-41.
Fang R., Pouyanfar S., Yang Y., Chen S.C., Iyengar S.S.(2016). Computational health informatics in the big data age: a survey, ACM Comput. Surv. 49 (1), 12, 1-36.
Ghafoorian M., Karssemeijer N., Heskes T., van Uder I.W.M., de Leeuw F.E., Marchiori E., van Ginneken B., Platel B. (2016). Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation, IEEE 13th International Symposium on Biomedical Imaging (ISBI), 1414-1417.
Gulshan V., Peng L., Coram M., et al. (2016). Development and Validation of a Deep Learning Algorithm for detection of Diabetic Retinopathy in retinal fundus photographs. JAMA,316, 2402-10.
Hinton G.E. & Salakhutdinov R.R. (2006). Reducing the Dimensionality of Data with Neural Networks, Science, 313(5786), 504-507.
Hinton G.E., Osindero S. & Teh Y.W. (2006) A Fast Learning Algorithm for Deep Belief Nets. Neural Comput., 18(7), 1527-1554.
Jiang F, Jiang Y, Zhi H, et al.(2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, doi:10.1136/svn-2017-000101
Jindal V., Birjandtalab J., Pouyan M.B., Nourani M. (2016). An adaptive deep learning approach for ppg-based identification, Engineering in Medicine and Biology Society, 6401–6404.
Kalchbrenner N., Grefenstette E., and Blunsom P. (2014). A Convolutional Neural Network for Modelling Sentences, Apr. 2014.
Kim Y.(2014). Convolutional Neural Networks for Sentence Classification, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1746-1751
Kim J., Hong J., Park H. (2018). Prospects of deep learning for medical imaging, Precision and Future Medicine, 2(2), 37-52.
Kong B., Zhan Y., Shin M., Denny T., Zhang S. (2016). Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network, Springer International Publishing, Cham, (2016), 264–272.
Kononenko I. (2001). Machine learning for medical diagnosis: history, state of the art and perspective, Artif. Intell. Med., 23 (1), 89–109.
Lecun Y. et al. (1989). Handwritten digit recognition: applications of neural network chips and automatic learning, IEEE Commun. Mag., 27(11), 41–46.
Lecun Y., Bottou L., Bengio Y., et al. (1998). Gradient-based learning applied to document recognition, Proc IEEE Inst Electr Electron Eng, 86, 2278–2324.
Long E., Lin H., Liu Z., et al. (2017). An artificial intelligence platform for the multihospital collaborative management of congenital cataracts.
Maxmen JS.(1976). The Post-Physician Era: Medicine in the 21st Century. New York, NY: Wiley.
Mikolov T. (2010). Recurrent neural network based language model, in Interspeech.
Peng Z., Wang G. (2017). An optimal energy-saving real-time task-scheduling algorithm for mobile terminals, Int. J. Distrib. Sensor Netw., 13(5), 1-12.
Peng Z., Wang G., Jiang H., Meng S.(2017). Research and improvement of ecg com- pression algorithm based on ezw, Computer Methods Programs Biomed., 145, 157-166.
Rahhal M.A., Bazi Y., AlHichri H., Alajlan N., Melgani F., Yager R. (2016). Deep learning approach for active classification of electrocardiogram signals, Inf. Sci., 345 (1), 340–354.
Rajpurkar P., Hannun A.Y., Haghpanahi M., Bourn C., Ng A.Y. (2017). Cardiologist- level arrhythmia detection with convolutional neural networks, ArXiv e-prints, 1-9.
Seide F, Agarwal A, eds. (2016). CNTK: microsoft's Open-Source Deep-Learning Toolkit. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Salakhutdinov R. & Hinton G. (2009). Semantic hashing, Int. J. Approx. Reason., 50, 969-978.
Schmidhuber J. (2015). Deep learning in neural networks: An overview, Neural Networks, 61, 85- 117.
Sirinukunwattana K., Raza S.E.A., Tsang Y.W., Snead D.R.J., Cree I.A., Rajpoot N.M. (2016). Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images, IEEE Trans. Med. Imaging, 35(5),1196-1206.
Taylor G.W., Hinton G.E., & Roweis S.T. (2006). Modeling Human Motion Using Binary Latent Variables, Advances in Neural Information Processing Systems, 19, 1345-1352.
Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA. (2010). Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res., 11, 3371-3408.
Worrall D.E., Wilson C.M., Brostow G.J.(2016). Automated Retinopathy of Prematu- rity Case Detection with Convolutional Neural Networks, Springer International Publishing, Cham, 68-76.
Yan Z., Zhan Y., Peng Z., Liao S., Shinagawa Y., Metaxas D.N., Zhou X.S. (2015). Body- part recognition using multi-stage deep learning, in: Information Processing in Medical Imaging: 24th International Conference, IPMI 2015, Springer Interna- tional Publishing, Cham, 449- 46.
Yu L., Yang X., Chen H., Qin J., Heng P.A. (2017). Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d mr images, Thirty-First AAAI Conference on Artificial Intelligence, 66-72.
Zilly J., Buhmann J.M., Mahapatra D.(2017). Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation, Comput. Med. Imaging Graph., 55, 28-41.
Zubair M., Kim J., Yoon C. (2016). An automated ecg beat classification system using convolutional neural networks, 6th International Conference on IT Convergence and Security (ICITCS), 1-5.