1 |
Y. S. Kim, H. J. Park, S. H. Park, H. J. Chun,, B. G. Choi, "A CT criteria of cardiomegaly", Journal of the Korean Radiological Society, Vol. 57, No. 3, pp. 235-238, 2007.
DOI
|
2 |
S. H. Yang, J. S. Lee, C. S. Kim, "The Accuracy of Echocardiography and ECG in the Left Ventricular Hypertrophy", The Journal of the Korea Contents Association, Vol. 16, No. 2, pp. 666-672, 2016. http://dx.doi.org/10.5392/JKCA.2016.16.02.666
DOI
|
3 |
H. Amin, W. J. Siddiqui, "Cardiomegaly", StatPearls [internet], 2020.
|
4 |
S. S. Alghamdi, I. Abdelaziz, M. Albadri, S. Alyanbaawi, R. Aljondi, A. Tajaldeen, "Study of cardiomegaly using chest x-ray", Journal of Radiation Research and Applied Sciences, Vol. 13, No. 1, pp. 460-467, 2020. http://dx.doi.org/10.1080/16878507.2020.1756187
DOI
|
5 |
Y. LeCun, Y. Bengio, G. Hinton, "Deep learning, Nature", Vol. 521, No. 7553, 436-444, 2015.
DOI
|
6 |
A. Krizhevsky, I. Sutskever, G. E. Hinton, "Imagenet classification with deep convolutional neural networks", Advances in neural information processing systems, 25, 1097-1105, 2012.
|
7 |
K. He, X. Zhang, S. Ren, J. Sun, "Deep residual learning for image recognition", In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
|
8 |
M. H. Yap, M. Goyal, F. Osman, E. Ahmad, R. Marti, E. Denton, A. Juette, R. Zwiggelaar, "End-to-end breast ultrasound lesions recognition with a deep learning approach", In Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, International Society for Optics and Photonics, Vol. 10578, pp. 1057819, 2018. https://doi.org/10.1117/12.2293498
DOI
|
9 |
B. Sahiner, A. Pezeshk, L. M. Hadjiiski, X. Wang, K. Drukker, K. H. Cha, R. M. Summers, M. L Giger, "Deep learning in medical imaging and radiation therapy", Medical physics, Vol. 46, No. 1, pp. e1-e36, 2019. http://dx.doi.org/10.1002/mp.13264
DOI
|
10 |
L. Yao, E. Poblenz, D. Dagunts, B. Covington, D. Bernard, K. Lyman, "Learning to diagnose from scratch by exploiting dependencies among labels", arXiv preprint arXiv:1710.10501, 2017.
|
11 |
M. T. Islam, M. A. Aowal, A. T. Minhaz, K. Ashraf, "Abnormality detection and localization in chest x-rays using deep convolutional neural networks", arXiv preprint arXiv:1705.09850, 2017.
|
12 |
K. Simonyan, A. Zisserman, "Very deep convolutional networks for large-scale image recognition", arXiv preprint arXiv:1409.1556, 2014.
|
13 |
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R. M. Summers, "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, pp. 2097-2106, 2017.
|
14 |
Q. Que, Z. Tang, R. Wang, Z. Zeng, J. Wang, M. Chua, T. S. Gee, X. Yang, B. Veeravalli, "CardioXNet: automated detection for cardiomegaly based on deep learning", In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 612-615, 2018.
|
15 |
O. Ronneberger, P. Fischer, T. Brox, "U-net: Convolutional networks for biomedical image segmentation", In International Conference on Medical image computing and computer-assisted intervention, pp. 234-241, 2015, Springer, Cham.
|
16 |
N. Japkowicz, M. Shah, "Evaluating learning algorithms: a classification perspective", Cambridge University Press, 2011.
|
17 |
S. H. Paek, J. M. Lee, S. J. Han, Y. W. Bahk, "Evaluation of cardiac measurements in healthy Korean adults", Journal of the Korean radiological society, Vol. 14, No. 1, pp. 57-62, 1978.
DOI
|
18 |
S. Lim, M. Lee, "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, Vol. 14, No. 4, pp. 69-77, 2018.
DOI
|
19 |
E. D. Frohlich, "Left ventricular hypertrophy as a risk factor", Cardiology clinics, Vol. 4, No. 1, pp. 137-144, 1986.
DOI
|
20 |
D. P. Kingma, J. Ba, "Adam: A method for stochastic optimization", arXiv preprint arXiv:1412.6980, 2014.
|
21 |
S. Han, H. K. Kang, J. Y. Jeong, M. H. Park, W. Kim, W. C. Bang, Y. K. Seong, "A deep learning framework for supporting the classification of breast lesions in ultrasound images", Physics in Medicine & Biology, Vol. 62, No. 19, pp. 7714, 2017. http://dx.doi.org/10.1088/1361-6560/aa82ec
DOI
|
22 |
C. S. Danzer, "The cardiothoracic ratio: an index of cardiac enlargement", The American Journal of the Medical Sciences, Vol. 157, No. 4, pp. 1827-1924, 1919.
DOI
|