[1]
|
Siegel, R.L., Miller, K.D. and Jemal, A. (2020) Cancer Statistics, 2020. CA: A Cancer Journal for Clinicians, 70, 7-30.
https://doi.org/10.3322/caac.21590
|
[2]
|
World Health Organization (2020) World Health Statistics 2020. World Health Organization, Geneva.
|
[3]
|
Armato III, S.G., Roberts, R.Y., Meyer, C.R., et al. (2007) The Lung Image Database Consortium (LIDC): Ensuring the Integrity of Expert-Defined “Truth”. Academic Radiology, 14, 1455-1463.
https://doi.org/10.1016/j.acra.2007.08.006
|
[4]
|
刘颖, 赖敏. 早期肺癌影像学诊断研究进展[J]. 影像研究与医学应用, 2019, 3(1): 10-11.
|
[5]
|
贾群玲. CT与MRI诊断孤立性肺结节良恶性的准确性分析[J]. 中国CT与MRI杂志, 2016, 14(10): 42-45.
|
[6]
|
Patel, V.K., Naik, S.K., Naidich, D.P., et al. (2013) A Practical Algorithmic Approach to the Diagnosis and Management of Solitary Pulmonary Nodules. Chest, 143, 840-846. https://doi.org/10.1378/chest.12-1487
|
[7]
|
National Lung Screening Trial Research Team (2011) The National Lung Screening Trial: Overview and Study Design. Radiology, 258, 243-253. https://doi.org/10.1148/radiol.10091808
|
[8]
|
Naidich, D.P., Marshall, C.H., Gribbin, C., et al. (1990) Low-Dose CT of the Lungs: Preliminary Observations. Radiology, 175, 729-731. https://doi.org/10.1148/radiology.175.3.2343122
|
[9]
|
Armato III, S.G., McLennan, G., Bidaut, L., et al. (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans. Medical Physics, 38, 915-931. https://doi.org/10.1118/1.3528204
|
[10]
|
Setio, A.A.A., Traverso, A., De Bel, T., et al. (2017) Validation, Comparison, and Combination of Algorithms for Automatic Detection of Pulmonary Nodules in Computed Tomography Images: The LUNA16 Challenge. Medical Image Analysis, 42, 1-13. https://doi.org/10.1016/j.media.2017.06.015
|
[11]
|
王婧璇, 林岚, 赵思远, 等. 基于深度学习的肺结节计算机断层扫描影像检测与分类的研究进展[J]. 生物医学工程学杂志, 2019, 36(4): 670-676.
|
[12]
|
Way, T.W., Sahiner, B., Chan, H.-P., et al. (2009) Computer-Aided Diagnosis of Pulmonary Nodules on CT Scans: Improvement of Classification Performance with Nodule Surface Features. Medical Physics, 36, 3086-3098.
https://doi.org/10.1118/1.3140589
|
[13]
|
Anirudh, R., Thiagarajan, J.J., Bremer, T. and Kim, H. (2016) Lung Nodule Detection Using 3D Convolutional Neural Networks Trained on Weakly Labeled Data. Medical Imaging 2016: Comput-er-Aided Diagnosis. International Society for Optics and Photonics, 9785, 978532. https://doi.org/10.1117/12.2214876
|
[14]
|
Liu, Y., Balagurunathan, Y., Atwater, T., et al. (2017) Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules. Clinical Cancer Research, 23, 1442-1449. https://doi.org/10.1158/1078-0432.CCR-15-3102
|
[15]
|
Hawkins, S., Wang, H., Liu, Y., et al. (2016) Predicting Malig-nant Nodules from Screening CT Scans. Journal of Thoracic Oncology, 11, 2120-2128. https://doi.org/10.1016/j.jtho.2016.07.002
|
[16]
|
Dilger, S.K., Uthoff, J., Judisch, A., et al. (2015) Improved Pulmonary Nodule Classification Utilizing Quantitative Lung Parenchyma Features. Journal of Medical Imaging, 2, 041004. https://doi.org/10.1117/1.JMI.2.4.041004
|
[17]
|
Aerts, H.J.W.L., Velazquez, E.R., Leijenaar, R.T.H., et al. (2014) De-coding Tumour Phenotype by Noninvasive Imaging Using a Quantitative Radiomics Approach. Nature Communications, 5, Article No. 4006.
https://doi.org/10.1038/ncomms5006
|
[18]
|
Wang, J., Liu, X., Dong, D., et al. (2016) Prediction of Malignant and Benign of Lung Tumor Using a Quantitative Radiomic Method. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, 16-20 August 2016, 1272-1275. https://doi.org/10.1109/EMBC.2016.7590938
|
[19]
|
Chen, S., Qin, J., Ji, X., et al. (2017) Automatic Scoring of Multiple Semantic Attributes with Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images. IEEE Transactions on Medical Imaging, 36, 802-814.
https://doi.org/10.1109/TMI.2016.2629462
|
[20]
|
杨佳玲, 赵涓涓, 强彦, 等. 基于深度信念网络的肺结节良恶性分类[J]. 科学技术与工程, 2016, 16(32): 69-74.
|
[21]
|
LeCun, Y., Boser, B., Denker, J.S., et al. (1989) Backpropagation Ap-plied to Handwritten Zip Code Recognition. Neural Computation, 1, 541-551. https://doi.org/10.1162/neco.1989.1.4.541
|
[22]
|
LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998) Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86, 2278-2324. https://doi.org/10.1109/5.726791
|
[23]
|
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017) Imagenet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60, 84-90. https://doi.org/10.1145/3065386
|
[24]
|
Donahue, J., Anne Hendricks, L., Guadarrama, S., et al. (2015) Long-Term Re-current Convolutional Networks for Visual Recognition and Description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, 7-12 June 2015, 2625-2634. https://doi.org/10.1109/CVPR.2015.7298878
|
[25]
|
He, K., Zhang, X., Ren, S. and Sun, J. (2016) Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 770-778.
https://doi.org/10.1109/CVPR.2016.90
|
[26]
|
Szegedy, C., Liu, W., Jia, Y., et al. (2015) Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, 7-12 June 2015, 1-9. https://doi.org/10.1109/CVPR.2015.7298594
|
[27]
|
Hua, K.L., Hsu, C.H., Hidayati, S.C., et al. (2015) Computer-Aided Classification of Lung Nodules on Computed Tomography Images via Deep Learning Technique. OncoTargets and Therapy, 8, 2015-2022.
https://doi.org/10.2147/OTT.S80733
|
[28]
|
Mastouri, R., Khlifa, N., Neji, H. and Hantous-Zannad, S. (2020) A Bilinear Convolutional Neural Network for Lung Nodules Classification on CT Images. International Journal of Computer Assisted Radiology and Surgery, 16, 91-101.
https://doi.org/10.1007/s11548-020-02283-z
|
[29]
|
吴世洋, 任劲松, 张冉, 等. 基于卷积神经网络的肺结节良恶性分类[J]. 中国医学工程, 2020, 28(1): 1-3.
|
[30]
|
Shen, W., Zhou, M., Yang, F., et al. (2015) Multi-Scale Convolutional Neural Networks for Lung Nodule Classification. In: International Conference on Information Processing in Medical Imaging, Springer, Cham, 588-599.
https://doi.org/10.1007/978-3-319-19992-4_46
|
[31]
|
Liu, S., Xie, Y., Jirapatnakul, A. and Reeves, A.P. (2017) Pulmo-nary Nodule Classification in Lung Cancer Screening with Three-Dimensional Convolutional Neural Networks. Journal of Medical Imaging, 4, 041308.
https://doi.org/10.1117/1.JMI.4.4.041308
|
[32]
|
Ciompi, F., Chung, K., Van Riel, S.J., et al. (2017) Towards Automatic Pulmonary Nodule Management in Lung Cancer Screening with Deep Learning. Scientific Reports, 7, Article No. 46479. https://doi.org/10.1038/srep46878
|
[33]
|
Zheng, J., Yang, D., Zhu, Y., et al. (2020) Pulmonary Nodule Risk Classification in Adenocarcinoma from CT Images Using Deep CNN with Scale Transfer Module. IET Image Processing, 14, 1481-1489.
https://doi.org/10.1049/iet-ipr.2019.0248
|
[34]
|
Shen, W., Zhou, M., Yang, F., et al. (2017) Multi-Crop Convolutional Neural Networks for Lung Nodule Malignancy Suspiciousness Classification. Pattern Recognition, 61, 663-673. https://doi.org/10.1016/j.patcog.2016.05.029
|
[35]
|
王强修, 李钧, 朱良明. 肺癌诊断与治疗[M]. 北京: 人民军医出版社, 2013.
|
[36]
|
刘一璟, 张旭斌, 张建伟, 等. DenseNet-centercrop: 一个用于肺结节分类的卷积网络[J]. 浙江大学学报(理学版), 2020, 47(1): 20-26.
|
[37]
|
Kumar, D., Wong, A. and Clausi, D.A. (2015) Lung Nodule Classification Using Deep Features in CT Images. 2015 12th Conference on Computer and Robot Vision, Halifax, 3-5 June 2015, 133-138.
https://doi.org/10.1109/CRV.2015.25
|
[38]
|
Kang, G., Liu, K., Hou, B. and Zhang, N. (2017) 3D Multi-View Convolu-tional Neural Networks for Lung Nodule Classification. PLoS ONE, 12, e0188290. https://doi.org/10.1371/journal.pone.0188290
|
[39]
|
Sun, W., Zheng, B. and Qian, W. (2017) Automatic Feature Learning Using Multichannel ROI Based on Deep Structured Algorithms for Computerized Lung Cancer Diagnosis. Computers in Biology and Medicine, 89, 530-539.
https://doi.org/10.1016/j.compbiomed.2017.04.006
|
[40]
|
Chen, M., Radford, A., Child, R., et al. (2020) Generative Pre-training from Pixels. Proceedings of the 37th International Conference on Machine Learning, 119, 1691-1703.
|
[41]
|
Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al. (2020) An Image Is Worth 16×16 Words: Transformers for Image Recognition at Scale. arXiv preprint arXiv:2010.11929.
|