AI 의료영상 분석의 개요 및 연구 현황에 대한 고찰

키워드

참고문헌 (59)

1. Harman M, "The role of artificial intelligence in software engineering", 2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE). IEEE, pp.1~6 (2012)
2. Pesapane F, "Artificial intelligence in medical imaging : Threat or opportunity? Radiologists again at the forefront of innovation in medicine", Eur Radiol Exp, 2(1), pp.35~ (2018)
3. Trinder JC, "Automatic Extraction of Man-Made Objects from Aerial and Space Images" (1997), Birkhäuser
4. Shen D, "Deep learning in medical image analysis", Annu Rev Biomed Eng, 19, pp.221~248 (2017)
5. RavR D, "Deep learning for health informatics", IEEE J Biomed Health Inform, 21(1), pp.4~21 (2017)
6. LeCun Y, "Backpropagation applied to handwritten zip code recognition", Neural Comput, 1(4), pp.541~551 (1989)
7. Neyshabur B, "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, "Deep learning with kernel regularization for visual recognition", Adv Neural Inf Process Syst, pp.1889~1896 (2009)
9. Kukačka J, "Regularization for deep learning: A taxonomy", arXiv preprint arXiv:1710.10686, ~ (2017)
10. Yoshida Y, "Spectral norm regularization for improving the generalizability of deep learning", arXiv preprint arXiv:1705.10941, ~ (2017)
11. Russell S, "Artificial intelligence a modern approach" (2009), Prentice Hall
12. Mitchell TM, "Machine learning" (1997), McGraw –Hill
13. Vial A, "The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling : A review", Translational Cancer Res, 7(3), pp.803~816 (2018)
14. Ang JC, "Supervised, unsupervised, and semi-supervised feature selection : A review on gene selection", IEEE, 2015, pp.13(5), 971~989
15. Lin YZ, "Structural damage detection with automatic feature-extraction through deep learning", Computer-Aided Civ Inf Eng, 32(12), pp.1025~1046 (2017)
16. Yang J, "Learning and transferring deep joint spectral–spatial features for hyperspectral classification", IEEE Trans Geosci Remote Sens, 55(8), pp.4729~4742 (2017)
17. Weiss KR, "Comparing transfer learning and traditional learning under domain class imbalance", 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.337~343 (2017)
18. Yoo Y, "Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation", International Workshop on Machine Learning in Medical Imaging, pp.117~124 (2014)
19. Cai Y, "Multi-modal vertebrae recognition using transformed deep convolution network", Comput Med Imaging Graph, 51, pp.11~19 (2016)
20. Jaumard-Hakoun A, "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" (2017), Hanbit Media
22. Kim H, "Performance analysis of CNN frameworks for GPUs", 2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp.55~64 (2017)
23. Agarap AF, "Deep learning using rectified linear units (relu)", arXiv preprint arXiv:1803.08375, ~ (2018)
24. 이준구, "Deep Learning in Medical Imaging: General Overview", Korean Journal of Radiology, 18(4), pp.570~584 (2017), 대한영상의학회[DOI]
25. Wakui Y, "Math to understand deep learning" (2018), Hanbit Media
26. Rajpurkar P, "Deep learning for chest radiograph diagnosis : A retrospective comparison of the CheXNeXt algorithm to practicing radiologists", PLoS Medicine, 15(11), pp.e1002686~ (2018)
27. Shadmi R, "Fully-convolutional deep-learning based system for coronary calcium score prediction from non-contrast chest CT", 2018 IEEE 15th International Symposium on Biomedical Imaging, pp.24~28 (2018)
28. Menze BH, "The multimodal brain tumor image segmentation benchmark(BRATS)", IEEE Transactions on Medical Imaging, 34(10), pp.1993~2024 (2014)
29. Urban G, "Multi-modal brain tumor segmentation using deep convolutional neural networks. MICCAI BraTS (brain tumor segmentation) challenge", Proceedings, Winning Contribution, pp.31~35 (2014)
30. Zikic D, "Segmentation of brain tumor tissues with convolutional neural networks", Proceedings MICCAI-BRATS, pp.36~39 (2014)
31. Öman O, "3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke", Eur Radiol Exp, 3(1), pp.8~ (2019)
32. Yang W, "A novel singular value decomposition-based denoising method in 4-dimensional computed tomography of the brain in stroke patients with statistical evaluation", Sensors, 20, pp.3063~ (2020)
33. 박하영, "급성 뇌졸중 환자의 시공간 분석 기법을 이용한 동적 전산화 단층 검사 : 예비 연구", 방사선기술과학, 39(4), pp.543~547 (2016), 대한방사선과학회[DOI]
34. 김도휘, "특이값분해 기반 동적의료영상 재구성기법의 특징 파악을 위한 시뮬레이션 연구", 방사선기술과학, 42(2), pp.119~130 (2019), 대한방사선과학회
35. Liu M, "Landmark-based deep multi-instance learning for brain disease diagnosis", Med Image Anal, 43, pp.157~168 (2018)
36. Choi H, "Alzheimer’s Disease Neuroimaging Initiative. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging", Behav Brain Res, 344, pp.103~109 (2018)
37. Huynh BQ, "Digital mammographic tumor classification using transfer learning from deep convolutional neural networks", J Med Imaging, 3(3), pp.034501~ (2016)
38. McKinney SM, "International evaluation of an AI system for breast cancer screening", Nature, 577(7788), pp.89~94 (2020)
39. Christe A, "Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images", Invest Radiol, 54(10), pp.627~ (2019)
40. Hamidinekoo A, "Deep learning in mammography and breast histology, an overview and future trends", Med Image Anal, 47, pp.45~67 (2018)
41. Cheng JZ, "Computer-aided diagnosis with deep learning architecture : applications to breast lesions in US images and pulmonary nodules in CT scans", Sci Rep, 6(1), pp.1~13 (2016)
42. Hua KL, "Computer-aided classification of lung nodules on computed tomography images via deep learning technique", Onco Targets Ther, 8 (2015)
43. Dross PE, "International radiology for developing countries : The delivery of medical imaging services to a Honduran radiologic scarce zone", J Am Coll Radiol, 11(12), pp.1173~1177 (2014)
44. Kwon DI, "Only about 10 people in the emergency room nationwide who specialize in imaging medicine to read CT · MRI", Hankook Ilbo, ~ (2017)
45. Wang X, "Chest X-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2097~2106 (2017)
46. Jack Jr CR, "The Alzheimer's disease neuroimaging initiative(ADNI) : MRI methods", J Magn Reson Imaging, 27(4), pp.685~691 (2008)
47. Malone IB, "MIRIAD—Public release of a multiple time point Alzheimer's MR imaging dataset", NeuroImage, 70, pp.33~36 (2013)
48. Moreira IC, "Inbreast : Toward a full-field digital mammographic database", Acad Radiol, 19(2), pp.236~248 (2012)
49. Razzak MI, "Classification in BioApps", pp.323~350 (2018)
50. Ding Q, "Low-dose CT with deep learning regularization via proximal forward backward splitting", Phys Med (2020)
51. 신윤주, "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 Journal of Radiology, 21(3), pp.356~364 (2020), 대한영상의학회[DOI]
52. Xie D, "Denoising arterial spin labeling perfusion MRI with deep machine learning", Magn Reson Imaging (2020)
53. Chen KT, "Ultra–low-dose 18F-florbetaben amyloid PET imaging using deep learning with multi-contrast MRI inputs", Radiology, 290(3), pp.649~656 (2019)
54. Goodfellow I, "Generative adversarial nets", Adv Neural Inf Processing Systems, pp.2672~2680 (2014)
55. Wolterink JM, "Deep MR to CT synthesis using unpaired data", International Workshop on Simulation and Synthesis in Medical Imaging, pp.14~23 (2017)
56. Yi X, "Sharpness-aware low-dose CT denoising using conditional generative adversarial network", J Digit Imaging, 31(5), pp.655~669 (2018)
57. SRnchez I, "Brain MRI super-resolution using 3D generative adversarial networks", arXiv preprint arXiv:1812.11440, ~ (2018)
58. Ran M, "Denoising of 3D magnetic resonance images using a residual encoder–decoder Wasserstein generative adversarial network", Medl Image Anal, 55, pp.165~180 (2019)
59. Nie D, "Medical image synthesis with context-aware generative adversarial networks", International Conference on Medical Image Computing and Computer-assisted Intervention, pp.417~425 (2017)

관련 논문AI추천