참고문헌 (77)

1. 10.1007/978-0-85729-057-1 Wang, L., Zhao, G., Cheng, L. & Pietikäinen, M. Machine Learning for Vision-Based Motion Analysis: Theory and Techniques (Springer, London, 2010). 

   

2. 10.1145/3123266.3130141 Mei, T. & Zhang, C. Deep learning for intelligent video analysis. Microsoft; https://www.microsoft.com/en-us/research/publication/deep-learning-intelligent-video-analysis/ (2017). 

   

3. Biomed. Res. Int. F Liang 2017 1279486 2017 Liang, F., Xie, W. & Yu, Y. Beating heart motion accurate prediction method based on interactive multiple model: an information fusion approach. Biomed. Res. Int. 2017, 1279486 (2017). 

   상세보기

4. Card. Fail. Rev. G Savarese 3 7 2017 10.15420/cfr.2016:25:2 Savarese, G. & Lund, L. H. Global public health burden of heart failure. Card. Fail. Rev. 3, 7-11 (2017). 

   상세보기

5. Eur. Heart J. N Galie 37 67 2016 10.1093/eurheartj/ehv317 Galie, N. et al. 2015 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension: The Joint Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS): Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC), International Society for Heart and Lung Transplantation (ISHLT). Eur. Heart J. 37, 67-119 (2016). 

   상세보기

6. Med. Image Anal. E Puyol-Antón 40 96 2017 10.1016/j.media.2017.06.002 Puyol-Antón, E. et al. A multimodal spatiotemporal cardiac motion atlas from MR and ultrasound data. Med. Image Anal. 40, 96-110 (2017). 

   상세보기

7. Heart Fail. Rev. A Scatteia 22 465 2017 10.1007/s10741-017-9621-8 Scatteia, A., Baritussio, A. & Bucciarelli-Ducci, C. Strain imaging using cardiac magnetic resonance. Heart Fail. Rev. 22, 465-476 (2017). 

   상세보기

8. Belkin, M. & Niyogi, P. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in Neural Information Processing Systems 14 (eds Dietterich, T. G. et al.) 585-591 (MIT Press, Cambridge, 2002). 

9. 10.1101/222208 Li, K., Javer, A., Keaveny, E. E. & Brown, A. E. X. Recurrent neural networks with interpretable cells predict and classify worm behaviour. Preprint at https://doi.org/10.1101/222208 (2017). 

   

10. 10.1007/978-3-319-46478-7_51 Walker, J., Doersch, C., Gupta, A. & Hebert, M. An uncertain future: forecasting from static images using variational autoencoders. Preprint at https://arxiv.org/abs/1606.07873 (2016). 

    

11. 10.1109/CVPR.2017.173 Bütepage, J., Black, M., Kragic, D. & Kjellström, H. Deep representation learning for human motion prediction and classification. Preprint at https://arxiv.org/abs/1702.07486 (2017). 

    

12. JACC Basic Transl. Sci. KW Johnson 2 311 2017 10.1016/j.jacbts.2016.11.010 Johnson, K. W. et al. Enabling precision cardiology through multiscale biology and systems medicine. JACC Basic Transl. Sci. 2, 311-327 (2017). 

    상세보기

13. Eur. Heart J. M Cikes 37 1642 2016 10.1093/eurheartj/ehv510 Cikes, M. & Solomon, S. D. Beyond ejection fraction: an integrative approach for assessment of cardiac structure and function in heart failure. Eur. Heart J. 37, 1642-1650 (2016). 

    상세보기

14. J. Am. Coll. Cardiol. T Ahmad 64 1765 2014 10.1016/j.jacc.2014.07.979 Ahmad, T. et al. Clinical implications of chronic heart failure phenotypes defined by cluster analysis. J. Am. Coll. Cardiol. 64, 1765-1774 (2014). 

    상세보기

15. Circulation SJ Shah 131 269 2015 10.1161/CIRCULATIONAHA.114.010637 Shah, S. J. et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 131, 269-279 (2015). 

    상세보기

16. Curr. Opin. Cardiol. SE Awan 33 190 2018 10.1097/HCO.0000000000000491 Awan, S. E., Sohel, F., Sanfilippo, F. M., Bennamoun, M. & Dwivedi, G. Machine learning in heart failure: ready for prime time. Curr. Opin. Cardiol. 33, 190-195 (2018). 

    상세보기

17. Comput. Struct. Biotechnol. J. EE Tripoliti 15 26 2017 10.1016/j.csbj.2016.11.001 Tripoliti, E. E., Papadopoulos, T. G., Karanasiou, G. S., Naka, K. K. & Fotiadis, D. I. Heart failure: diagnosis, severity estimation and prediction of adverse events through machine learning techniques. Comput. Struct. Biotechnol. J. 15, 26-47 (2017). 

    상세보기

18. Circ. Res. B Ambale-Venkatesh 121 1092 2017 10.1161/CIRCRESAHA.117.311312 Ambale-Venkatesh, B. et al. Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circ. Res. 121, 1092-1101 (2017). 

    상세보기

19. Sci. Rep. S Yousefi 7 2017 10.1038/s41598-017-11817-6 Yousefi, S. et al. Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Sci. Rep. 7, 11707 (2017). 

    상세보기

20. PLoS Comput. Biol. T Ching 14 1 2018 10.1371/journal.pcbi.1006076 Ching, T., Zhu, X. & Garmire, L. X. Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Comput. Biol. 14, 1-18 (2018). 

    상세보기

21. BMC Med. Res. Methodol. J Katzman 18 1 2018 10.1186/s12874-018-0482-1 Katzman, J. et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18, 1-12 (2018). 

    상세보기

22. Nature A Esteva 542 115 2017 10.1038/nature21056 Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115-118 (2017). 

    상세보기

23. J. R. Soc. Interface T Ching 15 20170387 2018 10.1098/rsif.2017.0387 Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine.J. R. Soc. Interface 15, 20170387 (2018). 

    상세보기

24. Med. Image Anal. G Litjens 42 60 2017 10.1016/j.media.2017.07.005 Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60-88 (2017). 

    상세보기

25. Annu. Rev. Biomed. Eng. D Shen 19 221 2017 10.1146/annurev-bioeng-071516-044442 Shen, D., Wu, G. & Suk, H. I. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221-248 (2017). 

    상세보기

26. J. Cardiovasc. Magn. Reson. W Bai 20 65 2018 10.1186/s12968-018-0471-x Bai, W. et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J. Cardiovasc. Magn. Reson. 20, 65 (2018). 

    상세보기

27. Sci. Rep. P Piras 7 2017 10.1038/s41598-017-12539-5 Piras, P. et al. Morphologically normalized left ventricular motion indicators from MRI feature tracking characterize myocardial infarction. Sci. Rep. 7, 12259 (2017). 

    상세보기

28. Gigascience X Zhang 6 1 2017 Zhang, X. et al. Orthogonal decomposition of left ventricular remodeling in myocardial infarction. Gigascience 6, 1-15 (2017). 

29. PLoS ONE X Zhang 9 e110243 2014 10.1371/journal.pone.0110243 Zhang, X. et al. Atlas-based quantification of cardiac remodeling due to myocardial infarction. PLoS ONE 9, e110243 (2014). 

    상세보기

30. Radiology T Dawes 283 381 2017 10.1148/radiol.2016161315 Dawes, T. et al. Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology 283, 381-390 (2017). 

    상세보기

31. 10.1007/978-3-642-23783-6_41 Rifai, S., Vincent, P., Muller, X., Glorot, X. & Bengio, Y. Contractive auto-encoders: explicit invariance during feature extraction. In Proc. 28th International Conference on Machine Learning, 833-840 (Omnipress, 2011). 

    상세보기

32. Rolfe, J. T. & LeCun, Y. Discriminative recurrent sparse auto-encoders. Preprint at 1301.3775 (2013). 

33. Math. Probl. Eng. R Huang 2016 14 2016 Huang, R., Liu, C., Li, G. & Zhou, J. Adaptive deep supervised autoencoder based image reconstruction for face recognition. Math. Probl. Eng. 2016, 14 (2016). 

34. 10.1007/s11063-018-9828-2 Du, F., Zhang, J., Ji, N., Hu, J. & Zhang, C. Discriminative representation learning with supervised auto-encoder. Neur. Proc. Lett. https://doi.org/10.1007/s11063-018-9828-2 (2018). 

    상세보기

35. Comp. Elec. Eng. S Zaghbani 68 337 2018 10.1016/j.compeleceng.2018.04.012 Zaghbani, S., Boujneh, N. & Bouhlel, M. S. Age estimation using deep learning. Comp. Elec. Eng. 68, 337-347 (2018). 

    상세보기

36. J. Biomed. Inform. BK Beaulieu-Jones 64 168 2016 10.1016/j.jbi.2016.10.007 Beaulieu-Jones, B. K. & Greene, C. S. Semi-supervised learning of the electronic health record for phenotype stratification. J. Biomed. Inform. 64, 168-178 (2016). 

    상세보기

37. 10.1007/978-3-319-51237-2_2 Shakeri, M., Lombaert, H., Tripathi, S. & Kadoury, S. Deep spectral-based shape features for Alzheimer’s disease classification. In International Workshop on Spectral and Shape Analysis in Medical Imaging (eds Reuter, M. et al.) 15-24 (Springer, 2016). 

    

38. Biffi, C. et al. Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling. In International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 11071 (eds Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) (Springer, 2018). 

39. 10.17605/OSF.IO/BG6T9 Dawes, T. J. W., Bello, G. A. & O’Regan, D. P. Multicentre study of machine learning to predict survival in pulmonary hypertension. Open Science Framework https://doi.org/10.17605/OSF.IO/BG6T9 (2018). 

    

40. Circ. Cardiovasc. Imaging J Grapsa 8 45 2015 10.1161/CIRCIMAGING.114.002107 Grapsa, J. et al. Echocardiographic and hemodynamic predictors of survival in precapillary pulmonary hypertension: seven-year follow-up. Circ. Cardiovasc. Imaging 8, 45-54 (2015). 

    상세보기

41. PLoS ONE W Bao 12 e0180944 2017 10.1371/journal.pone.0180944 Bao, W., Yue, J. & Rao, Y. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 12, e0180944 (2017). 

    상세보기

42. Lim, B. & van der Schaar, M. Disease-atlas: navigating disease trajectories with deep learning. Preprint at https://arxiv.org/abs/1803.10254 (2018). 

43. 10.1609/aaai.v32i1.11842 Lee, C., Zame, W. R., Yoon, J. & van der Schaar, M. DeepHit: a deep learning approach to survival analysis with competing risks. In 32nd Association for the Advancement of Artificial Intelligence ( AAAI) Conference (2018). 

    상세보기

44. Eur. Respir. Rev. D Gopalan 26 160108 2017 10.1183/16000617.0108-2016 Gopalan, D., Delcroix, M. & Held, M. Diagnosis of chronic thromboembolic pulmonary hypertension. Eur. Respir. Rev. 26, 160108 (2017). 

    상세보기

45. J. Cardiovasc. Magn. Reson. C Kramer 15 91 2013 10.1186/1532-429X-15-91 Kramer, C., Barkhausen, J., Flamm, S., Kim, R. & Nagel, E. Society for cardiovascular magnetic resonance board of trustees task force on standardized protocols. Standardized cardiovascular magnetic resonance (CMR) protocols 2013 update. J. Cardiovasc. Magn. Reson. 15, 91 (2013). 

    상세보기

46. J. Digit. Imaging M Woodbridge 26 886 2013 10.1007/s10278-013-9604-9 Woodbridge, M., Fagiolo, G. & O’Regan, D. P. MRIdb: medical image management for biobank research. J. Digit. Imaging 26, 886-890 (2013). 

    상세보기

47. J. Cardiovasc. Magn. Reson. J Schulz-Menger 15 35 2013 10.1186/1532-429X-15-35 Schulz-Menger, J. et al. Standardized image interpretation and post processing in cardiovascular magnetic resonance: society for cardiovascular magnetic resonance (SCMR) board of trustees task force on standardized post processing. J. Cardiovasc. Magn. Reson. 15, 35 (2013). 

    상세보기

48. Eur. Radiol. VJ Baggen 26 3771 2016 10.1007/s00330-016-4217-6 Baggen, V. J. et al. Cardiac magnetic resonance findings predicting mortality in patients with pulmonary arterial hypertension: a systematic review and meta-analysis. Eur. Radiol. 26, 3771-3780 (2016). 

    상세보기

49. 10.1093/ehjci/jey120 Hulshof, H. G. et al. Prognostic value of right ventricular longitudinal strain in patients with pulmonary hypertension: a systematic review and meta-analysis. Eur. Heart J. Cardiovasc. Imaging https://doi.org/10.1093/ehjci/jey120 (2018). 

    상세보기

50. Duan, J. et al. Automatic 3D bi-ventricular segmentation of cardiac images by a shape-constrained multi-task deep learning approach. Preprint at 1808.08578 (2018). 

51. Med. Image Anal. W Bai 26 133 2015 10.1016/j.media.2015.08.009 Bai, W. et al. A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion. Med. Image Anal. 26, 133-145 (2015). 

    상세보기

52. Med. Image Anal. W Shi 17 779 2013 10.1016/j.media.2013.04.010 Shi, W. et al. Temporal sparse free-form deformations. Med. Image Anal. 17, 779-789 (2013). 

    상세보기

53. IEEE Trans. Med. Imaging D Rueckert 18 712 1999 10.1109/42.796284 Rueckert, D. et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18, 712-721 (1999). 

    상세보기

54. 10.1007/978-3-319-20309-6_1 Bai, W et al. Learning a global descriptor of cardiac motion from a large cohort of 1000+ normal subjects. In 8th International Conference on Functional Imaging and Modeling of the Heart (FIMH’15) Vol. 9126 (Springer, Cham, 2015). 

    

55. J. Mach. Learn. Res. P Vincent 11 3371 2010 Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y. & Manzagol, P.-A. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371-3408 (2010). 

    상세보기

56. J. R. Stat. Soc. B D Cox 34 187 1972 Cox, D. Regression models and life-tables. J. R. Stat. Soc. B 34, 187-220 (1972). 

57. J. Mach. Learn. Res. N Srivastava 15 1929 2014 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929-1958 (2014). 

    상세보기

58. Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, Cambridge MA, 2016). 

59. Stat. Med. D Faraggi 14 73 1995 10.1002/sim.4780140108 Faraggi, D. & Simon, R. A neural network model for survival data. Stat. Med. 14, 73-82 (1995). 

    상세보기

60. Abadi, M. et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems (TensorFlow, 2015); http://download.tensorflow.org/paper/whitepaper2015.pdf 

61. Chollet, F. et al. Keras https://keras.io (2015). 

62. Proc. IEEE Int. Conf. Neural Net. J Kennedy 4 1942 1995 10.1109/ICNN.1995.488968 Kennedy, J. & Eberhart, R. Particle swarm optimization. Proc. IEEE Int. Conf. Neural Net. 4, 1942-1948 (1995). 

    

63. A Engelbrecht 2005 Fundamentals of Computational Swarm Intelligence Engelbrecht, A. Fundamentals of Computational Swarm Intelligence (Wiley, Chichester, 2005). 

64. 10.1145/3071178.3071208 Lorenzo, P. R., Nalepa, J., Kawulok, M., Ramos, L. S. & Pastor, J. R. Particle swarm optimization for hyper-parameter selection in deep neural networks. In Proc. Genetic and Evolutionary Computation Conference, GECCO ‘17, 481-488 (2017). 

    

65. Claesen, M., Simm, J., Popovic, D. & De Moor, B. Hyperparameter tuning in Python using Optunity.In Proc. International Workshop on Technical Computing for Machine Learning and Mathematical Engineering Vol. 9 (2014). 

66. J. Am. Med. Assoc. F Harrell 247 2543 1982 10.1001/jama.1982.03320430047030 Harrell, F., Califf, R., Pryor, D., Lee, K. & Rosati, R. Evaluating the yield of medical tests.J. Am. Med. Assoc. 247, 2543-2546 (1982). 

    상세보기

67. Ann. Intern. Med. K Moons 162 W1 2015 10.7326/M14-0698 Moons, K. et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann. Intern. Med. 162, W1-W73 (2015). 

    상세보기

68. Stat. Med. F Harrell 15 361 1996 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 Harrell, F., Lee, K. & Mark, D. Tutorial in biostatistics: multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15, 361-387 (1996). 

    상세보기

69. J. Am. Stat. Assoc. B Efron 78 316 1983 10.1080/01621459.1983.10477973 Efron, B. Estimating the error rate of a prediction rule: some improvements on cross-validation. J. Am. Stat. Assoc. 78, 316-331 (1983). 

    상세보기

70. B Efron 1993 An Introduction to the Bootstrap 10.1007/978-1-4899-4541-9 Efron, B. & Tibshirani, R. in An Introduction to the Bootstrap Ch. 17 (Chapman & Hall, New York, 1993). 

    

71. Am. J. Epidem. G Smith 180 318 2014 10.1093/aje/kwu140 Smith, G., Seaman, S., Wood, A., Royston, P. & White, I. Correcting for optimistic prediction in small data sets. Am. J. Epidem. 180, 318-324 (2014). 

    상세보기

72. Int. J. Cardiol. B Liu 252 220 2018 10.1016/j.ijcard.2017.10.106 Liu, B. et al. Normal values for myocardial deformation within the right heart measured by feature-tracking cardiovascular magnetic resonance imaging. Int. J. Cardiol. 252, 220-223 (2018). 

    상세보기

73. J. Heart Lung. Transplant. H Gall 36 957 2017 10.1016/j.healun.2017.02.016 Gall, H. et al. The Giessen pulmonary hypertension registry: survival in pulmonary hypertension subgroups. J. Heart Lung. Transplant. 36, 957-967 (2017). 

    상세보기

74. Bioinformatics DJ Stekhoven 28 112 2011 10.1093/bioinformatics/btr597 Stekhoven, D. J. & Buhlmann, P. missForest-non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112-118 (2011). 

    상세보기

75. Bioinformatics MS Schroder 27 3206 2011 10.1093/bioinformatics/btr511 Schroder, M. S., Culhane, A. C., Quackenbush, J. & Haibe-Kains, B. survcomp: an R/Bioconductor package for performance assessment and comparison of survival models. Bioinformatics 27, 3206-3208 (2011). 

    상세보기

76. 10.5281/zenodo.1451540 Bello, G. A. & O’Regan, D. Deep learning cardiac motion analysis for human survival prediction (4Dsurvival) Zenodo https://doi.org/10.5281/zenodo.1451540 (2019). 

    

77. 10.24433/CO.8519672.v1 Bello, G. et al. Deep learning cardiac motion analysis for human survival prediction (4Dsurvival). Code Ocean https://doi.org/10.24433/CO.8519672.v1 (2018).