利用深度学习技术辅助肺结节的人工智能检测_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

王成弟 ¹ , 郭际香 ² , 杨阳 ³ , 徐修远 ² , 胡亦清 ⁴ , 杨澜 ¹ , 章毅 ³ ,  李为民 ¹

1. ;
2. ;
3. ;
4. ;

Corresponding author：

李为民, Email: weimin003@163.com

Keywords：

DOI：

10.7507/1671-6205.201802042

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Citation： 王成弟, 郭际香, 杨阳, 徐修远, 胡亦清, 杨澜, 章毅, 李为民. 利用深度学习技术辅助肺结节的人工智能检测. Chinese Journal of Respiratory and Critical Care Medicine, 2019, 18(3): 288-294. doi: 10.7507/1671-6205.201802042 Copy

1.	Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132.
2.	中华医学会呼吸病学分会肺癌学组, 中国肺癌防治联盟专家组. 肺结节诊治中国专家共识(2018 年版). 中华结核和呼吸杂志, 2018, 41(10): 763-771.
3.	National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
4.	Rubin GD, Roos JE, Tall M, et al. Characterizing search, recognition, and decision in the detection of lung nodules on CT scans: elucidation with eye tracking. Radiology, 2015, 274(1): 276-286.
5.	Armato SG, Roberts RY, Kocherginsky M, et al. Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of "truth". Acad Radiol, 2009, 16(1): 28-38.
6.	崔云, 马大庆. 肺结节的 CT 计算机辅助检测和诊断的基本方法和应用. 中国医学影像术, 2007, 23(3): 469-472.
7.	Jacobs C, van Rikxoort EM, Twellmann T, et al. Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal, 2014, 18(2): 374-384.
8.	Cheng JZ, Ni D, Chou YH, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep, 2016, 6: 24454.
9.	Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning: a primer for radiologists. Radiographics, 2017, 37(7): 2113-2131.
10.	LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 1989, 1(4): 541-551.
11.	Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev, 1958, 65(6): 386-408.
12.	Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 1980, 36(4): 193-202.
13.	Lécun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86(11): 2278-2324.
14.	Chopra S, Hadsell R, LeCun Y. Learning a similarity metric discriminatively, with application to face verification. IEEE Computer Society Conference, 2015, 1: 539-546.
15.	Dan CC, Ueli M, Luca MG, et al. Deep, big, simple neural nets for handwritten digit recognition. Neural Comput, 2010, 22(12): 3207-3220.
16.	Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM, 2012, 60(2): 84-90.
17.	Sermanet P, Eigen D, Zhang X, et al. OverFeat: integrated recognition, localization and detection using convolutional networks. Proc ICLR, 2015, arXiv: 1312.6229v4. https://arxiv.org/pdf/1312.6229.pdf.
18.	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Proc ICLR, 2015, arXiv: 1409.1556v6. https://arxiv.org/pdf/1409.1556v6.pdf.
19.	Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015: 1-9. https://ieeexplore.ieee.org/document/7298594. doi: 10.1109/CVPR.2015.7298594.
20.	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016: 770-778. https://ieeexplore.ieee.org/document/7780459. doi: 10.1109/CVPR.2016.90.
21.	Huang G, Liu Z, Laurens VDM, et al. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017: 2261-2269. https://ieeexplore.ieee.org/document/8099726. doi: 10.1109/CVPR.2017.243.
22.	Girshick R, Donahue J, Darrell T, et al. Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell, 2016, 38(1): 142-158.
23.	He KM, Zhang XY, Ren SQ, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell, 2014, 37(9): 1904-1916.
24.	Girshick R. Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015: 1440-1448. doi: 10.1109/ICCV.2015.169.
25.	Ruhan S, Owens W, Wiegand R, et al. Intervertebral disc detection in X-ray images using faster R-CNN. Conf Proc IEEE Eng Med Biol Soc, 2017: 564-567.
26.	Dai JF, Li Y, He KM, et al. R-FCN: object detection via region-based fully convolutional networks. 2016. https://arxiv.org/pdf/1605.06409.pdf.
27.	Dai JF, Qi HZ, Xiong YW, et al. Deformable convolutional networks. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, italy, 2017: 764-773. doi: 10.1109/ICCV.
28.	Liu ST, Huang D, Wang YH. Receptive field block net for accurate and fast object detection. 2017. doi: 10.1007/978-3-030-01252-6_24.
29.	Al-Masni MA, Al-Antari MA, Park JM, et al. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Meth Prog Bio, 2018, 157: 85-94.
30.	Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector. European Conference on Computer Vision (ECCV) 2016: 21-37. https://link.springer.com/chapter/10.1007%2F978-3-319-46448-0_2. doi: 10.1007/978-3-319-46448-0_2.
31.	Litjens G, Sánchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep, 2016, 6: 26286.
32.	Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651.
33.	Somasundaram SK, Alli P. A machine learning ensemble classifier for early prediction of diabetic retinopathy. J Med Syst, 2017, 41(12): 201.
34.	Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533-536.
35.	Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput, 2006, 18(7): 1527-1554.
36.	Bengio Y, Lamblin P, Dan P, et al. Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst, 2007, 19: 153-160.
37.	Vincent P, Larochelle H, Bengio Y, et al. Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning Pages. ACM, 2008: 1096-1103. https://dl.acm.org/citation.cfm?doid=1390156.1390294. doi: 10.1145/1390156.1390294.
38.	Hinton G. A practical guide to training restricted Boltzmann machines. Momentum, 2010, 9(1): 926.
39.	Ginneken VB. Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning. Radiol Phys Technol, 2017, 10(1): 23-32.
40.	Li W, Cao P, Zhao DZ, et al. Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput Math Method Med, 2016: 6215085.
41.	Gruetzemacher R, Gupta A. Using deep learning for pulmonary nodule detection & diagnosis. Twenty-second Americas conference on information systems, San Diego 2016. https://aisel.aisnet.org/amcis2016/Intel/Presentations/3/.
42.	Hussein S, Gillies R, Cao K, et al. TumorNet: lung nodule characterization using multi-view convolutional neural network with gaussian process. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, 2017: 1007-1010. https://ieeexplore.ieee.org/document/7950686. doi: 10.1109/ISBI.2017.7950686.
43.	Nibali A, He Z, Wollersheim D. Pulmonary nodule classification with deep residual networks. Int J Comput Ass Rad, 2017, 12(10): 1799-1808.
44.	Song QZ, Zhao L, Luo XK, et al. Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng, 2017: 8314740.
45.	侍新, 谢世朋, 李海波. 基于卷积神经网络检测肺结节. 中国医学影像技术, 2018, 34(6): 934-939.
46.	吕晓琪, 吴凉, 谷宇, 等. 基于三维卷积神经网络的低剂量 CT 肺结节检测. 光学精密工程, 2018, 26(5): 213-220.
47.	巩萍, 王姗姗, 罗举建. 基于稀疏自编码神经网络的肺结节特征提取及良恶性分类. 医疗卫生装备, 2015, 36(12): 7-10.
48.	赵鑫, 强彦, 强梓林, 等. 基于局部感受野和半监督深度自编码的肺结节检测方法. 科学技术与工程, 2017, 17(33): 125-130.
49.	张婷, 赵涓涓, 罗嘉滢, 等. 基于多视角深度信念网络的肺结节识别方法. 科学技术与工程, 2018, 18(5): 92-98.
50.	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. 科学技术与工程, 2016, 16(32): 69-74.
51.	Sun WQ, Zheng B, Qian W. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med, 2017, 89: 530-539.
52.	Hua KL, Hsu CH, Hidayati SC, et al. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther, 2015, 8: 2015-2022.

1. Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132.
2. 中华医学会呼吸病学分会肺癌学组, 中国肺癌防治联盟专家组. 肺结节诊治中国专家共识(2018 年版). 中华结核和呼吸杂志, 2018, 41(10): 763-771.
3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
4. Rubin GD, Roos JE, Tall M, et al. Characterizing search, recognition, and decision in the detection of lung nodules on CT scans: elucidation with eye tracking. Radiology, 2015, 274(1): 276-286.
5. Armato SG, Roberts RY, Kocherginsky M, et al. Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of "truth". Acad Radiol, 2009, 16(1): 28-38.
6. 崔云, 马大庆. 肺结节的 CT 计算机辅助检测和诊断的基本方法和应用. 中国医学影像术, 2007, 23(3): 469-472.
7. Jacobs C, van Rikxoort EM, Twellmann T, et al. Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal, 2014, 18(2): 374-384.
8. Cheng JZ, Ni D, Chou YH, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep, 2016, 6: 24454.
9. Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning: a primer for radiologists. Radiographics, 2017, 37(7): 2113-2131.
10. LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput, 1989, 1(4): 541-551.
11. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev, 1958, 65(6): 386-408.
12. Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 1980, 36(4): 193-202.
13. Lécun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86(11): 2278-2324.
14. Chopra S, Hadsell R, LeCun Y. Learning a similarity metric discriminatively, with application to face verification. IEEE Computer Society Conference, 2015, 1: 539-546.
15. Dan CC, Ueli M, Luca MG, et al. Deep, big, simple neural nets for handwritten digit recognition. Neural Comput, 2010, 22(12): 3207-3220.
16. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM, 2012, 60(2): 84-90.
17. Sermanet P, Eigen D, Zhang X, et al. OverFeat: integrated recognition, localization and detection using convolutional networks. Proc ICLR, 2015, arXiv: 1312.6229v4. https://arxiv.org/pdf/1312.6229.pdf.
18. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Proc ICLR, 2015, arXiv: 1409.1556v6. https://arxiv.org/pdf/1409.1556v6.pdf.
19. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015: 1-9. https://ieeexplore.ieee.org/document/7298594. doi: 10.1109/CVPR.2015.7298594.
20. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016: 770-778. https://ieeexplore.ieee.org/document/7780459. doi: 10.1109/CVPR.2016.90.
21. Huang G, Liu Z, Laurens VDM, et al. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017: 2261-2269. https://ieeexplore.ieee.org/document/8099726. doi: 10.1109/CVPR.2017.243.
22. Girshick R, Donahue J, Darrell T, et al. Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell, 2016, 38(1): 142-158.
23. He KM, Zhang XY, Ren SQ, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell, 2014, 37(9): 1904-1916.
24. Girshick R. Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015: 1440-1448. doi: 10.1109/ICCV.2015.169.
25. Ruhan S, Owens W, Wiegand R, et al. Intervertebral disc detection in X-ray images using faster R-CNN. Conf Proc IEEE Eng Med Biol Soc, 2017: 564-567.
26. Dai JF, Li Y, He KM, et al. R-FCN: object detection via region-based fully convolutional networks. 2016. https://arxiv.org/pdf/1605.06409.pdf.
27. Dai JF, Qi HZ, Xiong YW, et al. Deformable convolutional networks. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, italy, 2017: 764-773. doi: 10.1109/ICCV.
28. Liu ST, Huang D, Wang YH. Receptive field block net for accurate and fast object detection. 2017. doi: 10.1007/978-3-030-01252-6_24.
29. Al-Masni MA, Al-Antari MA, Park JM, et al. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Meth Prog Bio, 2018, 157: 85-94.
30. Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector. European Conference on Computer Vision (ECCV) 2016: 21-37. https://link.springer.com/chapter/10.1007%2F978-3-319-46448-0_2. doi: 10.1007/978-3-319-46448-0_2.
31. Litjens G, Sánchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep, 2016, 6: 26286.
32. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651.
33. Somasundaram SK, Alli P. A machine learning ensemble classifier for early prediction of diabetic retinopathy. J Med Syst, 2017, 41(12): 201.
34. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533-536.
35. Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput, 2006, 18(7): 1527-1554.
36. Bengio Y, Lamblin P, Dan P, et al. Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst, 2007, 19: 153-160.
37. Vincent P, Larochelle H, Bengio Y, et al. Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning Pages. ACM, 2008: 1096-1103. https://dl.acm.org/citation.cfm?doid=1390156.1390294. doi: 10.1145/1390156.1390294.
38. Hinton G. A practical guide to training restricted Boltzmann machines. Momentum, 2010, 9(1): 926.
39. Ginneken VB. Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning. Radiol Phys Technol, 2017, 10(1): 23-32.
40. Li W, Cao P, Zhao DZ, et al. Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput Math Method Med, 2016: 6215085.
41. Gruetzemacher R, Gupta A. Using deep learning for pulmonary nodule detection & diagnosis. Twenty-second Americas conference on information systems, San Diego 2016. https://aisel.aisnet.org/amcis2016/Intel/Presentations/3/.
42. Hussein S, Gillies R, Cao K, et al. TumorNet: lung nodule characterization using multi-view convolutional neural network with gaussian process. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, 2017: 1007-1010. https://ieeexplore.ieee.org/document/7950686. doi: 10.1109/ISBI.2017.7950686.
43. Nibali A, He Z, Wollersheim D. Pulmonary nodule classification with deep residual networks. Int J Comput Ass Rad, 2017, 12(10): 1799-1808.
44. Song QZ, Zhao L, Luo XK, et al. Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng, 2017: 8314740.
45. 侍新, 谢世朋, 李海波. 基于卷积神经网络检测肺结节. 中国医学影像技术, 2018, 34(6): 934-939.
46. 吕晓琪, 吴凉, 谷宇, 等. 基于三维卷积神经网络的低剂量 CT 肺结节检测. 光学精密工程, 2018, 26(5): 213-220.
47. 巩萍, 王姗姗, 罗举建. 基于稀疏自编码神经网络的肺结节特征提取及良恶性分类. 医疗卫生装备, 2015, 36(12): 7-10.
48. 赵鑫, 强彦, 强梓林, 等. 基于局部感受野和半监督深度自编码的肺结节检测方法. 科学技术与工程, 2017, 17(33): 125-130.
49. 张婷, 赵涓涓, 罗嘉滢, 等. 基于多视角深度信念网络的肺结节识别方法. 科学技术与工程, 2018, 18(5): 92-98.
50. 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. 科学技术与工程, 2016, 16(32): 69-74.
51. Sun WQ, Zheng B, Qian W. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med, 2017, 89: 530-539.
52. Hua KL, Hsu CH, Hidayati SC, et al. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther, 2015, 8: 2015-2022.

Previous Article
咳嗽性晕厥综合征一例
Next Article
慢性阻塞性肺疾病合并胃食管反流病的研究进展

Chinese Journal of Respiratory and Critical Care Medicine

利用深度学习技术辅助肺结节的人工智能检测

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content