| 1 |
钱桂生. 肺癌不同病理类型发病率的变化情况及其原因[J/CD]. 中华肺部疾病杂志(电子版), 2011, 4(1): 1-5.
|
| 2 |
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019[J]. CA Cancer J Clin, 2019, 69(1): 7-34.
|
| 3 |
Zheng R, Zeng H, Zhang S, et al. Estimates of cancer incidence and mortality in China, 2013[J]. Chin J Cancer, 2017, 36(1): 66.
|
| 4 |
张晓菊，白 莉，金发光，等. 肺结节诊治中国专家共识(2018年版)[J]. 中华结核和呼吸杂志，2018, 41(10): 763-771.
|
| 5 |
Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening[J]. N Engl J Med, 2011, 365(5): 395-409.
|
| 6 |
Becker N, Motsch E, Trotter A, et al. Lung cancer mortality reduction by LDCT screening-results from the randomized german LUSI trial[J]. Int J Cancer, 2020, 146(6): 1503-1513.
|
| 7 |
Pastorino U, Silva M, Sestini S, et al. Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy[J]. Ann Oncol, 2019, 30(7): 1162-1169.
|
| 8 |
Amisha , Malik P, Pathania M, et al. Overview of artificial intelligence in medicine[J]. J Family Med Prim Care, 2019, 8(7): 2328-2331.
|
| 9 |
范卫杰，张 冬. 影像组学及深度学习在肺结节良恶性鉴别诊断中的新理念[J/CD]. 中华肺部疾病杂志(电子版), 2021, 14(5): 549-553.
|
| 10 |
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69(2): 127-157.
|
| 11 |
Lo SB, Freedman MT, Gillis LB, et al. JOURNAL CLUB: Computer-aided detection of lung nodules on CT with a computerized pulmonary vessel suppressed function[J]. AJR Am J Roentgenol, 2018, 210(3): 480-488.
|
| 12 |
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[J]. Jpn J Radiol, 2020, 38(11): 1052-1061.
|
| 13 |
Gong J, Liu JY, Wang LJ, et al. Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis[J]. Phys Med, 2018, 46: 124-133.
|
| 14 |
Gong L, Jiang S, Yang Z, et al. Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks[J]. Int J Comput Assist Radiol Surg, 2019, 14(11): 1969-1979.
|
| 15 |
Nasrullah N, Sang J, Alam M S, et al. Automated lung nodule detection and classification using deep learning combined with multiple strategies[J]. Sensors(Basel), 19(17): 3722.
|
| 16 |
Gu Y, Lu X, Zhang B, et al. Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography[J]. PLoS One, 2019, 14(1): e210551.
|
| 17 |
Tan J, Huo Y, Liang Z, et al. Expert knowledge-infused deep learning for automatic lung nodule detection[J]. J Xray Sci Technol, 2019, 27(1): 17-35.
|
| 18 |
Wang Q, Shen F, Shen L, et al. Lung nodule detection in CT images using a raw patch-based convolutional neural network[J]. J Digit Imaging, 2019, 32(6): 971-979.
|
| 19 |
Zheng S, Guo J, Cui X, et al. Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection[J]. IEEE Trans Med Imaging, 2020, 39(3): 797-805.
|
| 20 |
Yu J, Yang B, Wang J, et al. 2D CNN versus 3D CNN for false-positive reduction in lung cancer screening[J]. J Med Imaging (Bellingham), 2020, 7(5): 51202.
|
| 21 |
Jin H, Li Z, Tong R, et al. A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection[J]. Med Phys, 2018, 45(5): 2097-2107.
|
| 22 |
Lu X, Gu Y, Yang L, et al. Multi-level 3D Densenets for False-positive reduction in lung nodule detection based on chest computed tomography[J]. Curr Med Imaging, 2020, 16(8): 1004-1021.
|
| 23 |
Zuo W, Zhou F, He Y. An Embedded multi-branch 3D convolution neural network for false positive reduction in lung nodule detection[J]. J Digit Imaging, 2020, 33(4): 846-857.
|
| 24 |
Zhang W, Wang X, Li X, et al. 3D skeletonization feature based computer-aided detection system for pulmonary nodules in CT datasets[J]. Comput Biol Med, 2018, 92: 64-72.
|
| 25 |
Wang B, Si S, Zhao H, et al. False positive reduction in pulmonary nodule classification using 3D texture and edge feature in CT images[J]. Technol Health Care, 2019, 29(3): 1-18.
|
| 26 |
Wu W, Gao L, Duan H, et al. Segmentation of pulmonary nodules in CT images based on 3D-UNET combined with three-dimensional conditional random field optimization[J]. Med Phys, 2020, 47(9): 4054-4063.
|
| 27 |
Liu A, Wang Z, Yang Y, et al. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram[J]. Cancer Commun (Lond), 2020, 40(1): 16-24.
|
| 28 |
Feng B, Chen X, Chen Y, et al. Differentiating minimally invasive and invasive adenocarcinomas in patients with solitary sub-solid pulmonary nodules with a radiomics nomogram[J]. Clin Radiol, 2019, 74(7): 570-571.
|
| 29 |
Liu Q, Huang Y, Chen H, et al. The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma[J]. BMC Cancer, 2020, 20(1): 533.
|
| 30 |
Onishi Y, Teramoto A, Tsujimoto M, et al. Automated pulmonary nodule classification in computed tomography images using a deep convolutional neural network trained by generative adversarial networks[J]. Biomed Res Int, 2019, 2019: 6051939.
|
| 31 |
Onishi Y, Teramoto A, Tsujimoto M, et al. Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks[J]. Int J Comput Assist Radiol Surg, 2020, 15(1): 173-178.
|
| 32 |
Onishi Y, Teramoto A, Tsujimoto M, et al. Investigation of pulmonary nodule classification using multi-scale residual network enhanced with 3DGAN-synthesized volumes[J]. Radiol Phys Technol, 2020,13(2): 160-169.
|
| 33 |
Zhang G, Yang Z, Gong L, et al. Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations[J]. Radiol Med, 2020, 125(4): 374-383.
|
| 34 |
Li S, Xu P, Li B, et al. Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features[J]. Phys Med Biol, 2019, 64(17): 175012.
|
| 35 |
Tu SJ, Wang CW, Pan KT, et al. Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening[J]. Phys Med Biol, 2018, 63(6): 65005.
|
| 36 |
Kaya A. Cascaded classifiers and stacking methods for classification of pulmonary nodule characteristics[J]. Comput Methods Programs Biomed, 2018, 166: 77-89.
|