References
- Philbin EF, Garg R, Danisa K, Denny DM, Gosselin G, Hassapoyannes C. Digitalis Investigation Group. The relationship between cardiothoracic ratio and left ventricular ejection fraction in congestive heart failure. Archives of internal medicine. 1998;158(5):501-506. https://doi.org/10.1001/archinte.158.5.501
- Anderson JC, Baltaxe HA, Wolf GL. Inability to show clot: one limitation of ultrasonography of the abdominal aorta. Radiology. 1979;132(3):693-696. https://doi.org/10.1148/132.3.693
- Moon HJ, Kim EK, Park JS, Kwak JY. Thyroid Ultrasound: Change of Inter-observer Variability and Diagnostic Performance after Training. Journal of Korean Society of Ultrasound in Medicine. 2011;30(1):23-28.
- Frohlich ED. Left ventricular hypertrophy as a risk factor. Cardiology clinics. 1986;4(1):137-144. https://doi.org/10.1016/s0733-8651(18)30642-8
- Levy D, Anderson KM, Savage DD, Kannel WB, Christiansen JC, Castelli WP. Echocardiographically detected left ventricular hypertrophy: prevalence and risk factors: the Framingham Heart Study. Annals of internal medicine. 1988;108(1):7-13. https://doi.org/10.7326/0003-4819-108-1-7
- Lim S, Lee M. A Computer Aided Diagnosis Algorithm for Classification of Malignant Melanoma Based on Deep Learning. Journal of the Korea Society of Digital Industry and Information Management. 2018;14(4):69-77. https://doi.org/10.17662/KSDIM.2018.14.4.069
- Almeida J, Klima O. On the insertion of n-powers. arXiv preprint arXiv. 2017;1711.05525.
- Yao L, Poblenz E, Dagunts D, Covington B, Bernard D, Lyman K. Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint arXiv. 2017;1710.10501.
- 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. In Proceedings of the IEEE conference on computer vision and pattern recognition; 2017;2097-2106.
- Kim JY, Ye SY. Diagnostic Classification of Chest X-ray Pneumonia using Inception V3 Modeling. Journal of the Korean Society of Radiology. 2020;14(6):773-780. https://doi.org/10.7742/JKSR.2020.14.6.773
- Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition; 2015;1-9.
- Lambert J, Sener O, Savarese S. Deep learning under privileged information using heteroscedastic dropout. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018;8886-8895.
- Song HJ, Lee EB, Jo HJ, Park SY, Kim SY, Kim HJ, Hong JW. Evaluation of Classification and Accuracy in Chest Xray Images using Deep Learning with Convolution Neural Network. Journal of the Korean Society of Radiology. 2020;14(1):39-44. https://doi.org/10.7742/JKSR.2019.14.1.39
- Google. Advanced Guide to Inception v3 on Cloud TPU. https://cloud.google.com/tpu/docs/inception-v3-advanced?hl=en
- 이종근, 김선진, 곽내정, 김동우, 안재형. 흉부 디지털 영상의 병변 유무 판단을 위한 딥러닝 모델. 한국정보통신학회논문지. 2020;24(2):212-218. https://doi.org/10.6109/JKIICE.2020.24.2.212
- Song KD, Kim M, Do S. The latest trends in the use of deep learning in radiology illustrated through the stages of deep learning algorithm development. Journal of the Korean Society of Radiology. 2019;80(2):202-212. https://doi.org/10.3348/jksr.2019.80.2.202
- Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv; 2012;1207.0580.
- Philbin EF, Garg R, Danisa K, Denny DM, Gosselin G, Hassapoyannes C. Digitalis Investigation Group. The relationship between cardiothoracic ratio and left ventricular ejection fraction in congestive heart failure. Archives of internal medicine. 1998;158(5):501-506. https://doi.org/10.1001/archinte.158.5.501
- Tieleman T, Hinton G. Divide the gradient by a running average of its recent magnitude. coursera: Neural networks for machine learning. Technical Report; 2017.
- 정원준. 국내 인공지능 (AI) 의료기기 현황 및 규제 이슈. 주간기술동향. IITP; 2018.
- Japkowicz N, Shah M. Evaluating learning algorithms: a classification perspective. Cambridge University Press; 2011.