1. |
Wu F, Wang L, Zhou C. Lung cancer in China: current and prospect. Curr Opin Oncol, 2021, 33(1): 40-46.
|
2. |
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin, 2019, 69(1): 7-34.
|
3. |
Zheng S, Guo J, Cui X, et al. Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection. IEEE Trans Med Imaging, 2020, 39(3): 797-805.
|
4. |
de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized rrial. N Engl J Med, 2020, 382(6): 503-513.
|
5. |
蔡顺达, 吴湘萍. 多排螺旋 CT 对肺小结节及早期肺癌的诊断价值. 临床医药文献电子杂志, 2019, 6(A4): 171-172.
|
6. |
张述平, 李永立, 邢增, 等. 宝石 CT 高分辨率薄层扫描对早期肺癌的筛查. 影像研究与医学应用, 2018, 2(2): 31-32.
|
7. |
Loverdos K, Fotiadis A, Kontogianni C, et al. Lung nodules: A comprehensive review on current approach and management. Ann Thorac Med, 2019, 14(4): 226-238.
|
8. |
Massion PP, Antic S, Ather S, et al. Assessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules. Am J Respir Crit Care Med, 2020, 202(2): 241-249.
|
9. |
Agnes SA, Anitha J. Appraisal of deep-learning techniques on computer-aided lung cancer diagnosis with computed tomography screening. J Med Phys, 2020, 45(2): 98-106.
|
10. |
Kozuka T, Matsukubo Y, Kadoba T, et al. Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography. Jpn J Radiol, 2020, 38(11): 1052-1061.
|
11. |
Bell L, Gandhi S. A comparison of computer-assisted detection (CAD) programs for the identification of colorectal polyps: performance and sensitivity analysis, current limitations and practical tips for radiologists. Clin Radiol, 2018, 73(6): 593.e11-593.e18.
|
12. |
Ziyad SR, Radha V, Vayyapuri T. Overview of computer aided detection and computer aided diagnosis systems for lung nodule detection in computed tomography. Curr Med Imaging Rev, 2020, 16(1): 16-26.
|
13. |
Lin CH, Wu JX, Li CM, et al. Enhancement of chest X-ray images to improve screening accuracy rate using iterated function system and multilayer fractional-order machine learning classifier. IEEE Photonics J, 2020, 12(4): 1.
|
14. |
Gu Q, Feng Z, Liang Q, et al. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer. Eur J Radiol, 2019, 118: 32-37.
|
15. |
Sakamoto T, Furukawa T, Lami K, et al. A narrative review of digital pathology and artificial intelligence: focusing on lung cancer. Transl Lung Cancer Res, 2020, 9(5): 2255-2276.
|
16. |
Li H, Galperin-Aizenberg M, Pryma D, et al. Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiother Oncol, 2018, 129(2): 218-226.
|
17. |
臧启元, 黄钢, 徐磊, 等. 基于机器学习与细胞形态学对癌细胞分类. 软件, 2019, 40(9): 81-83.
|
18. |
王洪凯, 陈中华, 周纵苇, 等. 机器学习算法诊断 PET/CT 纵膈淋巴结性能评估. 浙江大学学报(工学版), 2018, 52(4): 788-797.
|
19. |
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
|
20. |
Oliver A, Sheryl, Anuradha M, et al. An efficient coding network based feature extraction with support vector machine based classification model for CT lung images. J Med Imaging Health Inform, 2020, 10(11): 2628-2633.
|
21. |
Wang Y, Zhou L, Wang M, et al. Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classification. Quant Imaging Med Surg, 2020, 10(6): 1249-1264.
|
22. |
Venkatesan, Nikitha J, Nam, CS, et al. Lung nodule vlassification on CT images using deep convolutional neural network based on geometric feature extraction. Med Imeaging Health Infor, 2020, 10(9): 2042-2052.
|
23. |
Nobrega D, Raul-Victor M, Reboucas F, et al. Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks. Neural Comput Appl, 2020, 32(15): 11065-11082.
|
24. |
Huang XF, Lei Q, Xie T, et al. Deep transfer convolutional neural network and extreme learning machine for lung nodule diagnosis on CT images. Knowledge-Based Systems, 2020, 204(27): 106230.
|
25. |
罗嘉滢, 赵涓涓, 强彦, 等. 基于多特征广义深度自编码的肺结节诊断方法. 计算机工程与设计, 2019, 40(1): 154-160.
|
26. |
王德才. 基于深度学习的肺癌检测方法研究. 数字技术与应用, 2020, 38(1): 85-89.
|