[1] Galbraith J D, Swann N, Cox B et al. The Bethesda classification for thyroid fine needle aspiration: a predictor or an alarmist?[J]. The American Surgeon, 84, 161-164(2018). http://europepmc.org/abstract/MED/29428046

[2] Kwak J Y, Han K H, Yoon J H et al. Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk[J]. Radiology, 260, 892-899(2011). http://www.ncbi.nlm.nih.gov/pubmed/21771959

[3] de Macedo B M, Izquierdo R F, Golbert L et al. . Reliability of thyroid imaging reporting and data system (TI-RADS), and ultrasonographic classification of the American thyroid association (ATA) in differentiating benign from malignant thyroid nodules[J]. Archives of Endocrinology and Metabolism, 62, 131-138(2018).

[4] Wang H, Yang Y, Peng B et al. A thyroid nodule classification method based on TI-RADS[J]. Proceedings of SPIE, 10420, 1042041(2017). http://adsabs.harvard.edu/abs/2017SPIE10420E..41W

[5] Cheng S P, Lee J J, Lin J L et al. Characterization of thyroid nodules using the proposed thyroid imaging reporting and data system (TI-RADS)[J]. Head & Neck, 35, 541-547(2013). http://labs.europepmc.org/abstract/MED/22514060

[6] Russ G. Risk stratification of thyroid nodules on ultrasonography with the French TI-RADS: description and reflections[J]. Ultrasonography, 35, 25-38(2016). http://europepmc.org/articles/PMC4701367/

[7] Lai W H, Zhou M R, Wang Y et al. Application of counterfeit liquor recognition based on deep learning and laser induced fluorescence[J]. Laser & Optoelectronics Progress, 55, 043001(2018).

[8] Jiang T. Computer aided diagnosis of thyroid nodules based on ultrasound images[D]. Nanchang: Nanchang University(2017).

[9] Ma J M, Luo S, Dighe M et al. Differential diagnosis of thyroid nodules with ultrasound elastography based on support vector machines. [C]∥2010 IEEE International Ultrasonics Symposium, October 11-14, 2010, San Diego, CA, USA. New York: IEEE, 1372-1375(2010).

[10] Keramidas E G, Maroulis D, Iakovidis D K. TND: a thyroid nodule detection system for analysis of ultrasound images and videos[J]. Journal of Medical Systems, 36, 1271-1281(2012).

[11] Liu X T, Ma X P, Liu L B. Thyroid nodule detection method based on median filter and residual network[J]. Computer Engineering and Applications, 55, 254-259(2019).

[12] Chollet F. Xception: deep learning with depthwise separable convolutions. [C]∥Proceedings of the IEEE conference on computer vision and pattern recognition(CVPR), July 21-26, 2017, Honolulu, Hawaii. New York: IEEE, 1251-1258(2017).

[13] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 9, 1735-1780(1997).

[14] Liu F, Liu P Y, Zhang J N et al. Joint detection of RGB-D images based on double flow convolutional neural network[J]. Laser & Optoelectronics Progress, 55, 021503(2018).

[15] Chi J N, Walia E, Babyn P et al. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network[J]. Journal of Digital Imaging, 30, 477-486(2017). http://www.ncbi.nlm.nih.gov/pubmed/28695342

[16] Xu Y, Sun M S. Convolution neural network image defogging based on multi-feature fusion[J]. Laser & Optoelectronics Progress, 55, 031012(2018).

[17] Liu X T, Liu L B, Zhang P. Cross-dataset image classification based on multi-convolutional neural network[J]. Computer Engineering and Design, 39, 257-262(2018).

[18] Pedraza L, Vargas C, Narváez F et al. An open access thyroid ultrasound image data-base[J]. Proceedings of SPIE, 9287, 92870W(2015). http://spie.org/Publications/Proceedings/Paper/10.1117/12.2073532

[19] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥Proceedings of the IEEE conference on computer vision and pattern recognition(CVPR), June 26-July 1, 2016, Las Vegas, Nevada. New York: IEEE, 770-778(2016).