Progress in biomedical data analysis based on deep learning_Journal of Biomedical Engineering

Authors：

LI Suyi ¹ , TANG Shijie ¹ , LI Feng ¹ , QI Jianzhuo ² ,  XIONG Wenji ³

1. College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, P.R.China;
2. Xining No.1 People’s Hospital, Xining 810000, P.R.China;
3. The First Hospital of Jilin University, Changchun 130021, P.R.China;

Corresponding author：

Keywords：

deep learning; biomedical science; data analysis

DOI：

10.7507/1001-5515.201907016

Video：

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Traditional biomedical data analysis technology faces enormous challenges in the context of the big data era. The application of deep learning technology in the field of biomedical analysis has ushered in tremendous development opportunities. In this paper, we reviewed the latest research progress of deep learning in the field of biomedical data analysis. Firstly, we introduced the deep learning method and its common framework. Then, focusing on the proposal of biomedical problems, data preprocessing method, model building method and training algorithm, we summarized the specific application of deep learning in biomedical data analysis in the past five years according to the chronological order, and emphasized the application of deep learning in medical assistant diagnosis. Finally, we gave the possible development direction of deep learning in the field of biomedical data analysis in the future.

Citation： LI Suyi, TANG Shijie, LI Feng, QI Jianzhuo, XIONG Wenji. Progress in biomedical data analysis based on deep learning. Journal of Biomedical Engineering, 2020, 37(2): 349-357. doi: 10.7507/1001-5515.201907016 Copy

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44.	刘振宇, 宋建聪. 基于深度学习的白内障自动诊断方法研究. 微处理机, 2019(3): 48-52.
45.	Acharya U R, Fujita H, Oh S L, et al. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences, 2017, 415: 190-198.
46.	李岭海. 基于深度学习的心脏病检测的研究. 现代计算机, 2017(9): 91-93, 110.
47.	Acharya U R, Oh S L, Hagiwara Y, et al. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine, 2018, 100: 270-278.
48.	Quang D, Xie X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Research, 2016, 44(11): e107-e107.
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1. Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
2. Hirschberg J, Manning C D. Advances in natural language processing. Science, 2015, 349(6245): 261-266.
3. Rybchak Z, Basystiuk O. Analysis of computer vision and image analysis technics. Econtechmod, 2017, 6(2): 79-84.
4. Mikolov T, Deoras A, Povey D, et al. Strategies for training large scale neural network language models// 2011 IEEE Workshop on Automatic Speech Recognition & Understanding. Hawaii: IEEE, 2011: 196-201.
5. Spencer M, Eickholt J, Cheng J. A deep learning network approach to ab initio protein secondary structure prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 2015, 12(1): 103-112.
6. Basheer I A, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 2000, 43(1): 3-31.
7. Safavian S R, Landgrebe D. A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 1991, 21(3): 660-674.
8. Wu J, Ji Y, Zhao L, et al. A mass spectrometric analysis method based on PPCA and SVM for early detection of ovarian cancer. Computational and Mathematical Methods in Medicine, 2016, 2016: 6169249.
9. Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18(7): 1527-1554.
10. Bengio Y, Lamblin P, Popovici D, et al. Greedy layer-wise training of deep networks// Advances in Neural Information Processing Systems 19 (NIPS 2006). Vancouver: MIT Press, 2006: 153-160.
11. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks// Advances in Neural Information Processing Systems 25 (NIPS 2012). Lake Tahoe: MIT Press, 2012: 1097-1105.
12. El Hihi S, Bengio Y. Hierarchical recurrent neural networks for long-term dependencies// Advances in Neural Information Processing Systems 8 (NIPS 1995). Denver: MIT Press, 1995: 493-499.
13. Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]// Advances in Neural Information Processing Systems 27 (NIPS 2014). Montreal: MIT Press, 2014: 2672-2680.
14. Bruna J, Zaremba W, Szlam A, et al. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv, 2013: 1312.6203.
15. Hubel D H, Wiesel T N. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 1962, 160(1): 106-154.
16. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE Computer Society, 2015: 1-9.
17. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation// International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.
18. Werbos P J. Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 1990, 78(10): 1550-1560.
19. Pascanu R, Mikolov T, Bengio Y. On the difficulty of training recurrent neural networks// International Conference on Machine Learning. Edinburgh: ACM, 2013: 1310-1318.
20. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735-1780.
21. Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv, 2016: 1609.02907.
22. Palm R B. Prediction as a candidate for learning deep hierarchical models of data. Copenhagen: Technical University of Denmark, 2012.
23. Jia Y, Shelhamer E, Donahue J, et al. Caffe: Convolutional architecture for fast feature embedding// Proceedings of the 22nd ACM International Conference on Multimedia. San Francisco: ACM, 2014: 675-678.
24. Bergstra J, Breuleux O, Bastien F, et al. Theano: a CPU and GPU math expression compiler// Proceedings of the Python for Scientific Computing Conference (SciPy). Austin: Enthought, 2010, 4(3).
25. Abadi M, Agarwal A, Barham P, et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv, 2016: 1603.04467.
26. Chen T, Li M, Li Y, et al. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv, 2015: 1512.01274.
27. Chollet F. Keras: Theano-based deep learning library. Code: https://github.com/fchollet. Documentation: http://keras.io, 2015.
28. Ketkar N. Introduction to pytorch// Deep learning with python. Berkeley: Apress, 2017: 195-208.
29. Team D. Deeplearning4j: Open-source distributed deep learning for the JVM. Apache Software Foundation License, 2016: 2.
30. Lipton Z C, Kale D C, Elkan C, et al. Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv, 2015: 1511.03677.
31. Haque A, Guo M, Miner A S, et al. Measuring depression symptom severity from spoken language and 3D facial expressions. arXiv preprint arXiv, 2018: 1811.08592.
32. Kamnitsas K, Ledig C, Newcombe V F J, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, 2017, 36: 61-78.
33. Wang S, Zhou M, Liu Z, et al. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Medical Image Analysis, 2017, 40: 172-183.
34. Zhao X, Wu Y, Song G, et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical Image Analysis, 2018, 43: 98-111.
35. Alom M Z, Hasan M, Yakopcic C, et al. Recurrent residual convolutional neural network based on U-net (R2U-net) for medical image segmentation. arXiv preprint arXiv, 2018: 1802.06955.
36. 叶海, 冯开平, 谢红宁. 基于全卷积网络的胎儿脑部超声图像分割算法. 现代计算机, 2019(17): 12.
37. Anthimopoulos M, Christodoulidis S, Ebner L, et al. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Transactions on Medical Imaging, 2016, 35(5): 1207-1216.
38. Shen W, Zhou M, Yang F, et al. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognition, 2017, 61: 663-673.
39. Esteva A, Kuprel B, Novoa R A, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639): 115-118.
40. Mohsen H, El-Dahshan E S A, El-Horbaty E S M, et al. Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 2018, 3(1): 68-71.
41. Xie Y, Zhang J, Xia Y, et al. Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT. Information Fusion, 2018, 42: 102-110.
42. Mao C, Yao L, Luo Y. ImageGCN: Multi-relational image graph convolutional networks for disease identification with chest X-rays. arXiv preprint arXiv, 2019: 1904.00325.
43. 鉏家欢, 潘乔. 融合图像和指标的阿尔茨海默病多分类诊断模型. 智能计算机与应用, 2019(4): 6-12.
44. 刘振宇, 宋建聪. 基于深度学习的白内障自动诊断方法研究. 微处理机, 2019(3): 48-52.
45. Acharya U R, Fujita H, Oh S L, et al. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences, 2017, 415: 190-198.
46. 李岭海. 基于深度学习的心脏病检测的研究. 现代计算机, 2017(9): 91-93, 110.
47. Acharya U R, Oh S L, Hagiwara Y, et al. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine, 2018, 100: 270-278.
48. Quang D, Xie X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Research, 2016, 44(11): e107-e107.
49. Pan X, Rijnbeek P, Yan J, et al. Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. BMC Genomics, 2018, 19(1): 511.
50. Liu Q, Xia F, Yin Q, et al. Chromatin accessibility prediction via a hybrid deep convolutional neural network. Bioinformatics, 2018, 34(5): 732-738.
51. 李洪顺, 于华, 宫秀军. 一种只利用序列信息预测 RNA 结合蛋白的深度学习模型. 计算机研究与发展, 2018, 55(1): 93-101.
52. Rossi E, Monti F, Bronstein M, et al. ncRNA classification with graph convolutional networks. arXiv preprint arXiv, 2019: 1905.06515.

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