3. Seiberling K A, Dutra J C, Grant T, et al. Role of intrathyroidal calcifications detected on ultrasound as a marker of malignancy. Laryngoscope, 2004, 114(10): 1753-1757.
[4]
4. Hoang J K, Lee W K, Lee M, D, et al. US features of thyroid malignancy: pearls and pitfalls,. Radiographics, 2007, 27(3): 847-860.
[5]
5. Chen Kuenyuan, Chen C N, Wu M H, et al. Computerized detection and quantification of microcalcifications in thyroid nodules. Ultrasound Med Biol, 2011, 37(6): 870-878.
[6]
6. Choi W J, Park J S, Kim K G, et al. Computerized analysis of calcification of thyroid nodules as visualized by ultrasonography. Eur J Radiol, 2015, 84(10): 1949-1953.
8. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. The 25th International Conference on Neural Information Processing System, Lake Tahoe, 2012: 1097-1105.
[9]
9. Lawrence S, Giles C L, Tsoi A C, et al. Face recognition: a convolutional neural-network approach. IEEE Transactions on Neural Networks, 1997, 8(1): 98-113.
[10]
10. Howard A G. . Some improvements on deep convolutional neural network based image classification. Computer Science, 2013, arXiv: 1312. 5402.
[11]
11. Gu Jiuxiang, Wang Zhenhua, Jason K, et al. Recent advances in convolutional neural networks, 2017.
[12]
12. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Washington: IEEE Computer Society, 2015: 1-8.