Application of Deep Learning Methods in Diagnosis of Lung Nodules

Bin Cao; Feng Yang; Jingang Ma

doi:10.3788/LOP202158.1600005

[1] Yang F, Wei G H, Cao H et al. Research progress on content-based medical image retrieval[J]. Laser & Optoelectronics Progress, 57, 060003(2020).

[2] Kermany D S, Goldbaum M, Cai W J et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 172, 1122-1131(2018).

[3] le Cun Y, Boser B, Denker J S et al. Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1, 541-551(1989).

[4] Kodirov E, Xiang T, Gong S G. Semantic autoencoder for zero-shot learning[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 4447-4456(2017).

[5] Zou M, Conzen S D. A new Dynamic Bayesian Network (DBN) approach for identifying gene regulatory networks from time course microarray data[J]. Bioinformatics, 21, 71-79(2005).

[6] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[7] Armato S G, Roberts R Y, Mcnitt-Gray M F et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans[J]. Academic Radiology, 14, 1455-1463(2007).

[8] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[10] Kuan K, Ravaut M, Manek G et al. Deep learning for lung cancer detection: tackling the kaggle data science bowl 2017 challenge[EB/OL]. (2017-05-26)[2020-11-05]. https://arxiv.org/abs/1705.09435

[13] Aggarwal P, Vig R, Sardana H K. Semantic and content-based medical image retrieval for lung cancer diagnosis with the inclusion of expert knowledge and proven pathology[C]. //2013 IEEE Second International Conference on Image Information Processing, December 9-11,2013, Shimla, India, 14029735(2014).

[14] van Ginneken B, Armato S G III, de Hoop B III et al. Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study[J]. Medical Image Analysis, 14, 707-722(2010).

[16] Havaei M, Davy A, Warde-Farley D et al. Brain tumor segmentation with deep neural networks[J]. Medical Image Analysis, 35, 18-31(2017).

[17] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]. //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 3431-3440(2015).

[18] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. //Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[19] Miao G, Li C F. Detection of pulmonary nodules CT images combined with two-dimensional and three-dimensional convolution neural networks[J]. Laser & Optoelectronics Progress, 55, 051006(2018).

[20] Chen S H, Qin J, Ji X et al. Automatic scoring of multiple semantic attributes with multi-task feature leverage: a study on pulmonary nodules in CT images[J]. IEEE Transactions on Medical Imaging, 36, 802-814(2017).

[21] Han Y, Ye J C. Framing U-net via deep convolutional framelets: application to sparse-view CT[J]. IEEE Transactions on Medical Imaging, 37, 1418-1429(2018).

[22] Tong G F, Li Y, Chen H R et al. Improved U-Net network for pulmonary nodules segmentation[J]. Optik, 174, 460-469(2018).

[23] Ruan H Y, Chen Z L, Cheng Y S et al. Detection of pulmonary nodules based on C-3D deformable convolutional neural network model[J]. Laser & Optoelectronics Progress, 57, 041013(2020).

[24] Setio A A A, Traverso A, de Bel T et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge[J]. Medical Image Analysis, 42, 1-13(2017).

[25] Dou Q, Chen H, Jin Y M et al. Automated pulmonary nodule detection via 3D ConvNets with online sample filtering and hybrid-loss residual learning[M]. //Descoteaux M, Maier-Hein L, Franz A, et al. Medical image computing and computer assisted intervention-MICCAI 2017. Lecture notes in computer science, 10435, 630-638(2017).

[26] Liu S, Xie Y, Jirapatnakul A et al. Pulmonary nodule classification in lung cancer screening with three-dimensional convolutional neural networks[J]. Journal of Medical Imaging, 4, 041308(2017).

[27] Roy R, Chakraborti T, Chowdhury A S. A deep learning-shape driven level set synergism for pulmonary nodule segmentation[J]. Pattern Recognition Letters, 123, 31-38(2019).

[28] Diciotti S, Picozzi G, Falchini M et al. 3-D segmentation algorithm of small lung nodules in spiral CT images[J]. IEEE Transactions on Information Technology in Biomedicine, 12, 7-19(2008).

[29] Cao H C, Liu H, Song E M et al. Dual-branch residual network for lung nodule segmentation[J]. Applied Soft Computing, 86, 105934(2020).

[30] Hou T X, Zhao J J, Qiang Y et al. Pulmonary nodules segmentation based on CRF 3D-UNet structure[J]. Computer Engineering and Design, 41, 1663-1669(2020).

[31] Dehmeshki J, Amin H, Valdivieso M et al. Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach[J]. IEEE Transactions on Medical Imaging, 27, 467-480(2008).

[32] Ciompi F, Chung K, van Riel S J et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning[J]. Scientific Reports, 7, 46479(2017).

[33] Xie H T, Yang D B, Sun N N et al. Automated pulmonary nodule detection in CT images using deep convolutional neural networks[J]. Pattern Recognition, 85, 109-119(2019).

[34] Sun W Q, Zheng B, Qian W. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis[J]. Computers in Biology and Medicine, 89, 530-539(2017).

[35] Yuan Z G, Bi L X, Hui H G. Survey on medical image computer aided detection and diagnosis systems[J]. Journal of Software, 15, 56-64(2018).

[36] Nibali A, He Z, Wollersheim D. Pulmonary nodule classification with deep residual networks[J]. International Journal of Computer Assisted Radiology and Surgery, 12, 1799-1808(2017).

[37] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[38] Xie Y T, Xia Y, Zhang J P et al. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT[J]. IEEE Transactions on Medical Imaging, 38, 991-1004(2019).

[39] Ding J, Li A X, Hu Z Q et al. Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks[M]. //Descoteaux M, Maier-Hein L, Franz A, et al. Medical image computing and computer assisted intervention-MICCAI 2017. Lecture notes in computer science, 10435, 559-567(2017).

[40] Zheng S Y, Guo J P, Cui X N et al. Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection[J]. IEEE Transactions on Medical Imaging, 39, 797-805(2020).

[41] Yang J L, Zhao J J, Qiang Y et al. A classification method of pulmonary nodules based on deep belief network[J]. Science Technology and Engineering, 16, 69-74(2016).

[42] Liu L, Yang P L, Sun W W et al. DBN classifier for classification of benign and malignant solitary pulmonary nodule[J]. Journal of Harbin University of Science and Technology, 23, 9-15(2018).

[43] Xu J Q, Hong L P, Zhu H B et al. Generative adversarial networks for the classification of lung nodules malignant[J]. Journal of Northeastern University (Natural Science), 39, 1556-1561(2018).

[44] Choi Y, Choi M, Kim M et al. StarGAN: unified generative adversarial networks for multi-domain image-to-image translation[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, 8789-8797(2018).

[45] Hsu S Y, Yang C Y, Huang C C et al. SemiStarGAN: semi-supervised generative adversarial networks for multi-domain image-to-image translation[M]. //Jawahar C V, Li H D, Mori G, et al. Computer vision-ACCV 2018. Lecture notes in computer science, 11364, 338-353(2019).

[46] 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]. Current Medical Imaging, 16, 1004-1021(2020).

[47] Luo J Y, Zhao J J, Qiang Y et al. Lung nodules diagnosis using multi-features generalized deep auto-encoder based on extreme learning machine[J]. Computer Engineering and Design, 40, 154-160(2019).

[48] Shen W, Zhou M, Yang F et al. Multi-scale convolutional neural networks for lung nodule classification[M]. //Shen W, Zhou M, Yang F, et al. Information processing in medical imaging. Lecture notes in computer science, 9123, 588-599(2015).

[49] Shen W, Zhou M, Yang F et al. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification[J]. Pattern Recognition, 61, 663-673(2017).

[50] Dey R, Lu Z J, Hong Y. Diagnostic classification of lung nodules using 3D neural networks[C]. //2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), April 4-7, 2018, Washington, DC, USA., 774-778(2018).

[51] Setio A A A, Ciompi F, Litjens G et al. Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks[J]. IEEE Transactions on Medical Imaging, 35, 1160-1169(2016).

[52] Kuo C F J, Huang C C, Siao J J et al. Automatic lung nodule detection system using image processing techniques in computed tomography[J]. Biomedical Signal Processing and Control, 56, 101659(2020).

[53] Lin Z Z, Wang G T, Fu Q S et al. Benign and malignant classification model of pulmonary nodules based on residual neural network[C]. //Proceedings of the 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019), November 24-25, 2019, Beijing, China., 164-167(2019).

[54] Xia X, Brain K. W-net: a deep model for fully unsupervised image segmentation[EB/OL]. (2017-11-22)[2020-11-05]. https://arxiv.org/abs/1711.08506

[55] Siegel R L, Miller K D, Jemal A. Cancer statistics, 2020[J]. CA: a Cancer Journal for Clinicians, 70, 7-30(2020).

[56] Yuan X F, Huang B, Wang Y L et al. Deep learning-based feature representation and its application for soft sensor modeling with variable-wise weighted SAE[J]. IEEE Transactions on Industrial Informatics, 14, 3235-3243(2018).

[57] Lu N, Li T F, Ren X D et al. A deep learning scheme for motor imagery classification based on restricted boltzmann machines[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25, 566-576(2017).

[58] Hussein S, Cao K L, Song Q et al. Risk stratification of lung nodules using 3D CNN-based multi-task learning[M]. //Niethammer M, Styner M, Aylward S, et al. Information processing in medical imaging. Lecture notes in computer science, 10265, 249-260(2017).