Otwarty dostęp

Overview of Deep Learning Models in Biomedical Domain with the Help of R Statistical Software


Zacytuj

1. Goodfellow I, Bengio Y, Courville A, Bengio Y. 2016. Deep learning (Vol. 1). Cambridge: MA, USA, MIT press. Search in Google Scholar

2. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521(7553): 436.10.1038/nature1453926017442 Search in Google Scholar

3. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics. 2018; 19(6): 1236-46.10.1093/bib/bbx044645546628481991 Search in Google Scholar

4. Urban G, Bache KM, Phan D, et al. Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2018; 16(3): 1029-35.10.1109/TCBB.2018.2841396790423529993583 Search in Google Scholar

5. Lakhani P. Deep convolutional neural networks for endotracheal tube position and X-ray image classification: challenges and opportunities. Journal of digital imaging. 2017; 30(4): 460-8.10.1007/s10278-017-9980-7553709428600640 Search in Google Scholar

6. LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks. 1995; 3361(10). Search in Google Scholar

7. Gao M, Bagci U, Lu L, et al. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2018; 6(1): 1-6.10.1080/21681163.2015.1124249588194029623248 Search in Google Scholar

8. Mohamed AA, Berg WA, Peng H, Luo Y, Jankowitz RC, Wu S. A deep learning method for classifying mammographic breast density categories. Medical physics. 2018; 45(1); 314-21.10.1002/mp.12683577423329159811 Search in Google Scholar

9. Esteva A, Kuprel B, Novoa RA, et al. Dermatologistlevel classification of skin cancer with deep neural networks. Nature. 2017; 542(7639), 115. Search in Google Scholar

10. Saltz J, Gupta R, Hou L, et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell reports. 2018; 23(1): 181-93.10.1016/j.celrep.2018.03.086594371429617659 Search in Google Scholar

11. Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018; 172(5): 1122-31.10.1016/j.cell.2018.02.01029474911 Search in Google Scholar

12. Gerard SE, Patton TJ, Christensen GE, Bayouth JE, Reinhardt JM. FissureNet: A deep learning approach for pulmonary fissure detection in CT images. IEEE transactions on medical imaging. 2018; 38(1): 156-66.10.1109/TMI.2018.2858202631801230106711 Search in Google Scholar

13. Hinton, G. Deep learning—a technology with the potential to transform health care. JAMA. 2018; 320(11): 1101-2.10.1001/jama.2018.1110030178065 Search in Google Scholar

14. Chen M, Hao Y, Hwang K, Wang L, Wang L. Disease prediction by machine learning over big data from healthcare communities. IEEE Access. 2017; 5: 8869-79.10.1109/ACCESS.2017.2694446 Search in Google Scholar

15. Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: an unsupervised representation to predict the future of patients from the electronic healthy records. Scientific reports. 2016; 6: 26094.10.1038/srep26094486911527185194 Search in Google Scholar

16. Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D. Early diagnosis of Alzheimer’s disease with deep learning. In Biomedical Imaging (ISBI). IEEE 11th International Symposium. 2014, p. 1015-1018.10.1109/ISBI.2014.6868045 Search in Google Scholar

17. Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNAand RNA-binding proteins by deep learning. Nature biotechnology. 2015; 33(8): 831.10.1038/nbt.330026213851 Search in Google Scholar

18. Park Y, Kellis M. Deep learning for regulatory genomics. Nature biotechnology. 2015; 33(8): 825.10.1038/nbt.331326252139 Search in Google Scholar

19. Chen Y, Li Y, Narayan R, Subramanian A, Xie X. Gene expression inference with deep learning. Bioinformatics. 2016; 32(12): 1832-9.10.1093/bioinformatics/btw074490832026873929 Search in Google Scholar

20. Weng WH, Wagholikar KB, McCray AT, Szolovits P, Chueh HC. Medical subdomain classification of clinical notes using a machine learning- based natural language processing approach. BMC medical informatics and decision making. 2017; 17(1): 155.10.1186/s12911-017-0556-8570984629191207 Search in Google Scholar

21. Collins FS, Varmus H. A new initiative on precision medicine. New England Journal of Medicine. 2015; 372(9): 793-5.10.1056/NEJMp1500523510193825635347 Search in Google Scholar

22. Nezhad MZ, Zhu D, Li X, Yang K, Levy P. Safs: A deep feature selection approach for precision medicine. In Bioinformatics and Biomedicine (BIBM), IEEE International Conference, 15-18, Dec 2016, p. 501-06. Shenzhen, China, IEEE.10.1109/BIBM.2016.7822569 Search in Google Scholar

23. Lu L, Zheng Y, Carneiro G, Yang L. 2017. Deep Learning and Convolutional Neural Networks for Medical Image Computing. (1st Ed) MA, USA. Springer.10.1007/978-3-319-42999-1 Search in Google Scholar

24. Lo, SCB, Chan HP, Lin JS, Li H, Freedman MT, Mun SK. Artificial convolution neural network for medical image pattern recognition. Neural networks. 1995; 8(7-8): 1201-14.10.1016/0893-6080(95)00061-5 Search in Google Scholar

25. Ciresan DC, Meier U, Gambardella LM, Schmidhuber J. Convolutional neural network committees for handwritten character classifica-tion. In: Document Analysis and Recognition (ICDAR), International Conference, 2011, p. 1135-9. Beijing, China, IEEE.10.1109/ICDAR.2011.229 Search in Google Scholar

26. Mou L, Ghamisi P, Zhu XX. Deep recurrent neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing. 2017; 55(7): 3639-55.10.1109/TGRS.2016.2636241 Search in Google Scholar

27. Tran SD, Manmatha R. U.S. Patent No. 9, 892,344. Washington, DC: U.S. Patent and Trademark. Search in Google Scholar

28. Zhang Y, Shi B. Improving pooling method for regularization of convolutional networks based on the failure probability density. Optik-International Journal for Light and Electron Optics. 2017; 145, 258-65. Search in Google Scholar

29. Del Fiol G, Michelson M, Iorio A, Cotoi C, Haynes RB. A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study. Journal of medical Internet research. 2018; 20(6): e10281.10.2196/10281603794429941415 Search in Google Scholar

30. Choi K, Fazekas G, Sandler M, Cho K. (2017, March). Convolutional recurrent neural networks for music classification. In Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference. 2017, p. 2392-6. New Orleans, USA, IEEE.10.1109/ICASSP.2017.7952585 Search in Google Scholar

31. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence. 2018; 40(4): 834-48.10.1109/TPAMI.2017.269918428463186 Search in Google Scholar

32. Kimmel J, Brack A, Marshall WF. Deep convolution neural networks allow analysis of cell motility during stem cell differentiation and neoplastic transformation. bioRxiv. 2017; 159202. Search in Google Scholar

33. Graves A, Mohamed AR, Hinton G. Speech recognition with deep recurrent neural networks. In Acoustics, speech and signal processing (icassp), IEEE international conference, 2013 p. 6645-9. Vancouver, Canada, IEEE.10.1109/ICASSP.2013.6638947 Search in Google Scholar

34. Sun X, Li T, Li Y, Li Q, Huang Y, Liu J. Recurrent neural system with minimum complexity: A deep learning perspective. Neurocomputing. 2018; 275: 1333-49.10.1016/j.neucom.2017.09.075 Search in Google Scholar

35. Tan JH, Hagiwara Y, Pang W, et al. Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Computers in Biology and Medicine. 2018; 94:19–26.10.1016/j.compbiomed.2017.12.02329358103 Search in Google Scholar

36. Lipton ZC, Kale DC, Elkan C, Wetzel R. Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv. 2015; 1511.03677. Search in Google Scholar

37. Jnawali K, Arbabshirani MR, Rao N, Patel AA. (2018, February). Deep 3D convolution neural network for CT brain hemorrhage classification. In Medical Imaging 2018: Computer-Aided Diagnosis (Vol. 10575, p. 105751C).10.1117/12.2293725 Search in Google Scholar

38. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in biology and medicine, 2018; 100: 270-8.10.1016/j.compbiomed.2017.09.01728974302 Search in Google Scholar

39. Li W, Shi S, Gao Z, et l. Improved deep belief network model and its application in named entity recognition of Chinese electronic medical records. In Big Data Analysis (ICBDA), IEEE 3rd International Conference, 2018 p. 356-60. Shanghai, China, IEEE.10.1109/ICBDA.2018.8367707 Search in Google Scholar

40. Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics. 2018; 22(5): 1589-1604.10.1109/JBHI.2017.2767063604342329989977 Search in Google Scholar

41. Zech J, Pain M, Titano J, et l. Natural Language– based Machine Learning Models for the Annotation of Clinical Radiology Reports. Radiology. 2018; 287(2), 570-80. Search in Google Scholar

42. Del Fiol G, Michelson M, Iorio A, et al. A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study. Journal of medical Internet research, 2018; 20(6).10.2196/10281603794429941415 Search in Google Scholar

43. Young T, Hazarika D, Poria S, Cambria E. Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine, 2018; 13(3), 55-75.10.1109/MCI.2018.2840738 Search in Google Scholar

44. Deng L, Liu Y. A joint introduction to natural language processing and to deep learning. In: Deep learning in natural language processing. 2018, p. 1-22. Springer, Singapore.10.1007/978-981-10-5209-5_1 Search in Google Scholar

45. R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved September 21, 2018 from http://www.R-project.org/. Search in Google Scholar

46. Chen T, Kou Q, He T. mxnet. MXNet [2015]. Retrieved September 21, 2018 from https://github.com/dmlc/mxnet/R-package. Search in Google Scholar

47. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR, 21-26 July 21-26 2017(2097-2106). Honolulu, Hawaii,IEEE.10.1109/CVPR.2017.369 Search in Google Scholar

48. Heath M, Bowyer K, Kopans D, et al. Current status of the digital database for screening mammography. In Digital mammography (pp. 457-460). Springer, Dordrecht.10.1007/978-94-011-5318-8_75 Search in Google Scholar

49. J Suckling et al. The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series. 1994; 1069: 375-8. Search in Google Scholar

50. Diagnostic image Analysis Group [2009] Retrieved September 21, 2018 from http://www.diagnijmegen.nl/index.php/NWO__Bayesian_Decision_Support_in_Medical_Screening_%28B-SCREEN%29 Search in Google Scholar

51. MITOS-ATYPIA [2014] Retrieved September 21, 2018 from https://mitos-atypia-14.grand-challenge.org/ Search in Google Scholar

52. Pau G, Fuchs F, Sklyar O, Boutros M, Huber W. EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics. 2010; 26(7): 979-81.10.1093/bioinformatics/btq046284498820338898 Search in Google Scholar

eISSN:
2335-075X
ISSN:
1820-8665
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Medicine, Clinical Medicine, other