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참고문헌 (59)

1. Harman M. The role of artificial intelligence in software engineering. In: 2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE). IEEE. 2012 Jun:1-6. 

2. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: Threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018 Oct;2(1):35. 

   상세보기

3. Trinder JC, Wang Y, Sowmya A, Palhang M. Artificial intelligence in 3-D feature extraction. In Automatic Extraction of Man-Made Objects from Aerial and Space Images. 2nd ed. Basel: Birkhauser; 1997. 

4. Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017 Jun;19:221-48. 

   상세보기

5. RavR D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, et al. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017 Jan;21(1):4-21. 

   상세보기

6. LeCun Y, Boser B, Denker JS, Henderson D. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989;1(4):541-51. 

   상세보기

7. Neyshabur B, Tomioka R, Srebro N. In search of the real inductive bias: On the role of implicit regularization in deep learning. arXiv preprint arXiv: 1412.6614; 2014. 

8. Yu K, Xu W, Gong Y. Deep learning with kernel regularization for visual recognition. Adv Neural Inf Process Syst. 2009:1889-96. 

9. Kukacka J, Golkov V, Cremers D. Regularization for deep learning: A taxonomy. arXiv preprint arXiv:1710.10686; 2017. 

10. Yoshida Y, Miyato T. Spectral norm regularization for improving the generalizability of deep learning. arXiv preprint arXiv:1705.10941; 2017. 

11. Russell S, Norvig P. Artificial intelligence a modern approach. 3rd ed. New Jersey: Prentice Hall; 2009. 

12. Mitchell TM. Machine learning. New York: McGraw-Hill; 1997. 

13. Vial A, Stirling D, Field M, Ros M, Ritz CH, Carolan, MG, et al. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: A review. Translational Cancer Res. 2018;7(3):803-16. 

    상세보기

14. Ang JC, Mirzal A, Haron H, Hamed HNA. Supervised, unsupervised, and semi-supervised feature selection: A review on gene selection. IEEE/ACM Trans Comput Biol Bioinform. 2015;13(5):971-89. 

15. Lin YZ, Nie ZH, Ma HW. Structural damage detection with automatic feature-extraction through deep learning. Computer-Aided Civ Inf Eng. 2017;32(12):1025-46. 

    상세보기

16. Yang J, Zhao YQ, Chan JCW. Learning and transferring deep joint spectral-spatial features for hyperspectral classification. IEEE Trans Geosci Remote Sens. 2017;55(8):4729-42. 

    상세보기

17. Weiss KR, Khoshgoftaar TM. Comparing transfer learning and traditional learning under domain class imbalance. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). 2017 Dec:337-43. 

18. Yoo Y, Brosch T, Traboulsee A, Li DK, Tam R. Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation. In International Workshop on Machine Learning in Medical Imaging. 2014 Sep:117-24. 

19. Cai Y, Landis M, Laidley DT, Kornecki A, Lum A, Li S. Multi-modal vertebrae recognition using transformed deep convolution network. Comput Med Imaging Graph. 2016 Jul;51:11-9. 

    상세보기

20. Jaumard-Hakoun A, Xu K, Roussel-Ragot P, Dreyfus G, Denby, B. Tongue contour extraction from ultrasound images based on deep neural network. arXiv preprint arXiv:1605.05912; 2016. 

21. Saito K. Deep learning starting from the bottom. Seoul: Hanbit Media; 2017. 

22. Kim H, Nam H, Jung W, Lee J. Performance analysis of CNN frameworks for GPUs. In 2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). 2017 Apr:55-64. 

23. Agarap AF. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375; 2018. 

24. Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB. Deep learning in medical imaging: General overview. Korean J Radiol. 2017 Aug;18(4):570-84. 

    상세보기

25. Wakui Y, Wakui S. Math to understand deep learning. Seoul: Hanbit Media; 2018. 

26. Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine. 2018;15(11):e1002686. 

    상세보기

27. Shadmi R, Mazo V, Bregman-Amitai O, Elnekave E. Fully-convolutional deep-learning based system for coronary calcium score prediction from non-contrast chest CT. In 2018 IEEE 15th International Symposium on Biomedical Imaging. 2018 Apr:24-8. 

28. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging. 2014;34(10):1993-2024. 

29. Urban G, Bendszus M, Hamprecht F, Kleesiek J. Multi-modal brain tumor segmentation using deep convolutional neural networks. MICCAI BraTS (brain tumor segmentation) challenge. Proceedings, Winning Contribution. 2014:31-5. 

30. Zikic D, Ioannou Y, Brown M, Criminisi A. Segmentation of brain tumor tissues with convolutional neural networks. Proceedings MICCAI-BRATS. 2014:36-9. 

31. Oman O, Makela T, Salli E, Savolainen S, Kangasniemi M. 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke. Eur Radiol Exp. 2019;3(1):8. 

    상세보기

32. Yang W, Hong JY, Kim JY, Paik SH, Lee SH, Park JS, et al. A novel singular value decomposition-based denoising method in 4-dimensional computed tomography of the brain in stroke patients with statistical evaluation. Sensors. 2020;20:3063. 

    상세보기

33. Park HY, Pyeon D, Kim DH, Jung Y. Dynamic computed tomography based on spatio-temporal analysis in acute stroke: Preliminary study. J Radiol Sci Technol. 2016;39:543-7. 

    원문보기 상세보기

34. Kim D, Jung Y. Simulation study for feature identification of dynamic medical image reconstruction technique based on singular value decomposition. J Radiol Sci Technol. 2019;42:119-30. 

    원문보기 상세보기

35. Liu M, Zhang J, Adeli E, Shen D. Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal. 2018;43:157-68. 

    상세보기

36. Choi H, Jin KH. Alzheimer's Disease Neuroimaging Initiative. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res. 2018;344:103-9. 

    상세보기

37. Huynh BQ, Li H, Giger ML. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging. 2016;3(3):034501. 

    상세보기

38. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. 

    상세보기

39. Christe A, Peters AA, Drakopoulos D, Heverhagen JT, Geiser T, Stathopoulou T, et al. Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images. Invest Radiol. 2019;54(10):627. 

    상세보기

40. Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R. Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal. 2018;47:45-67. 

    상세보기

41. Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep. 2016;6(1):1-13. 

    상세보기

42. Hua KL, Hsu CH, Hidayati SC, Cheng WH, Chen YJ. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther. 2015;8. 

    상세보기

43. Dross PE, Sumner R, Tysowsky M, Aujero MP. International radiology for developing countries: The delivery of medical imaging services to a Honduran radiologic scarce zone. J Am Coll Radiol. 2014;11(12):1173-7. 

    상세보기

44. Kwon DI. Only about 10 people in the emergency room nationwide who specialize in imaging medicine to read CT.MRI. Hankook Ilbo, 2017.11.20. 

45. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chest X-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:2097-106. 

46. Jack Jr CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685-91. 

    상세보기

47. Malone IB, Cash D, Ridgway GR, MacManus DG, Ourselin S, Fox NC, et al. MIRIAD-Public release of a multiple time point Alzheimer's MR imaging dataset. NeuroImage. 2013;70:33-6. 

    상세보기

48. Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. Inbreast: Toward a full-field digital mammographic database. Acad Radiol. 2012;19(2):236-48. 

    상세보기

49. Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: Overview, challenges and the future. In Classification in BioApps. 2018:323-50. 

50. Ding Q, Chen G, Zhang X, Huang Q, Ji H, Gao H. Low-dose CT with deep learning regularization via proximal forward backward splitting. Phys Med. 2020. 

51. Shin YJ, Chang W, Ye JC, Kang E, Oh DY, Lee YJ. Low-dose abdominal CT using a deep learning-based denoising algorithm: A comparison with CT reconstructed with filtered back projection or iterative reconstruction algorithm. Korean J Radiol. 2020;21(3):356-64. 

    상세보기

52. Xie D, Li Y, Yang H, Bai L, Wang T, Zhou F. Denoising arterial spin labeling perfusion MRI with deep machine learning. Magn Reson Imaging. 2020. 

53. Chen KT, Gong E, de Carvalho Macruz FB, Xu J, Boumis A, Khalighi M. Ultra-low-dose 18F-florbetaben amyloid PET imaging using deep learning with multi-contrast MRI inputs. Radiology. 2019; 290(3):649-56. 

    상세보기

54. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S. Generative adversarial nets. Adv Neural Inf Processing Systems. 2014:2672-80. 

55. Wolterink JM, Dinkla AM, Savenije MH, Seevinck PR, van den Berg CA, Isgum I. Deep MR to CT synthesis using unpaired data. In International Workshop on Simulation and Synthesis in Medical Imaging. 2017 Sep:14-23. 

56. Yi X, Babyn P. Sharpness-aware low-dose CT denoising using conditional generative adversarial network. J Digit Imaging. 2018;31(5):655-69. 

    상세보기

57. SRnchez I, Vilaplana V. Brain MRI super-resolution using 3D generative adversarial networks. arXiv preprint arXiv:1812.11440; 2018. 

58. Ran M, Hu J, Chen Y, Chen H, Sun H, Zhou J. Denoising of 3D magnetic resonance images using a residual encoder-decoder Wasserstein generative adversarial network. Medl Image Anal. 2019; 55:165-80. 

    상세보기

59. Nie D, Trullo R, Lian J, Petitjean C, Ruan S, Wang Q. Medical image synthesis with context-aware generative adversarial networks. In International Conference on Medical Image Computing and Computer-assisted Intervention. 2017 Sep:417-25. 

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