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Published in: Journal of Imaging Informatics in Medicine 1/2024

10-01-2024 | Pulmonary Nodule

TiCNet: Transformer in Convolutional Neural Network for Pulmonary Nodule Detection on CT Images

Authors: Ling Ma, Gen Li, Xingyu Feng, Qiliang Fan, Lizhi Liu

Published in: Journal of Imaging Informatics in Medicine | Issue 1/2024

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Abstract

Lung cancer is the leading cause of cancer death. Since lung cancer appears as nodules in the early stage, detecting the pulmonary nodules in an early phase could enhance the treatment efficiency and improve the survival rate of patients. The development of computer-aided analysis technology has made it possible to automatically detect lung nodules in Computed Tomography (CT) screening. In this paper, we propose a novel detection network, TiCNet. It is attempted to embed a transformer module in the 3D Convolutional Neural Network (CNN) for pulmonary nodule detection on CT images. First, we integrate the transformer and CNN in an end-to-end structure to capture both the short- and long-range dependency to provide rich information on the characteristics of nodules. Second, we design the attention block and multi-scale skip pathways for improving the detection of small nodules. Last, we develop a two-head detector to guarantee high sensitivity and specificity. Experimental results on the LUNA16 dataset and PN9 dataset showed that our proposed TiCNet achieved superior performance compared with existing lung nodule detection methods. Moreover, the effectiveness of each module has been proven. The proposed TiCNet model is an effective tool for pulmonary nodule detection. Validation revealed that this model exhibited excellent performance, suggesting its potential usefulness to support lung cancer screening.
Literature
1.
go back to reference R. L. Siegel, K. D. Miller, N. S. Wagle, and A. Jemal, “Cancer statistics, 2023,” CA: a cancer journal for clinicians, vol. 73, no. 1, pp. 17–48, 2023. R. L. Siegel, K. D. Miller, N. S. Wagle, and A. Jemal, “Cancer statistics, 2023,” CA: a cancer journal for clinicians, vol. 73, no. 1, pp. 17–48, 2023.
2.
go back to reference J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in globocan 2012,” Int J Cancer, vol. 136, no. 5, pp. E359–E386, 2015.CrossRefPubMed J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in globocan 2012,” Int J Cancer, vol. 136, no. 5, pp. E359–E386, 2015.CrossRefPubMed
3.
go back to reference D. J. Brenner and E. J. Hall, “Computed tomography–an increasing source of radiation exposure,” New England journal of medicine, vol. 357, no. 22, pp. 2277–2284, 2007.CrossRefPubMed D. J. Brenner and E. J. Hall, “Computed tomography–an increasing source of radiation exposure,” New England journal of medicine, vol. 357, no. 22, pp. 2277–2284, 2007.CrossRefPubMed
4.
go back to reference S. G. Armato III, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P. Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman, et al., “The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans,” Medical physics, vol. 38, no. 2, pp. 915–931, 2011.ADSCrossRefPubMedPubMedCentral S. G. Armato III, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P. Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman, et al., “The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans,” Medical physics, vol. 38, no. 2, pp. 915–931, 2011.ADSCrossRefPubMedPubMedCentral
5.
go back to reference S. Singh, D. S. Gierada, P. Pinsky, C. Sanders, N. Fineberg, Y. Sun, D. Lynch, and H. Nath, “Reader variability in identifying pulmonary nodules on chest radiographs from the national lung screening trial,” Journal of thoracic imaging, vol. 27, no. 4, p. 249, 2012.CrossRefPubMedPubMedCentral S. Singh, D. S. Gierada, P. Pinsky, C. Sanders, N. Fineberg, Y. Sun, D. Lynch, and H. Nath, “Reader variability in identifying pulmonary nodules on chest radiographs from the national lung screening trial,” Journal of thoracic imaging, vol. 27, no. 4, p. 249, 2012.CrossRefPubMedPubMedCentral
6.
go back to reference I. R. S. Valente, P. C. Cortez, E. C. Neto, J. M. Soares, V. H. C. de Albuquerque, and J. M. R. Tavares, “Automatic 3d pulmonary nodule detection in ct images: a survey,” Computer methods and programs in biomedicine, vol. 124, pp. 91–107, 2016.CrossRefPubMed I. R. S. Valente, P. C. Cortez, E. C. Neto, J. M. Soares, V. H. C. de Albuquerque, and J. M. R. Tavares, “Automatic 3d pulmonary nodule detection in ct images: a survey,” Computer methods and programs in biomedicine, vol. 124, pp. 91–107, 2016.CrossRefPubMed
7.
go back to reference K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 37, no. 9, pp. 1904–1916, 2015.CrossRefPubMed K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 37, no. 9, pp. 1904–1916, 2015.CrossRefPubMed
8.
go back to reference S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems, vol. 28, 2015. S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems, vol. 28, 2015.
9.
go back to reference T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117–2125, 2017. T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117–2125, 2017.
10.
go back to reference N. Sharma, L. M. Aggarwal, et al., “Automated medical image segmentation techniques,” Journal of medical physics, vol. 35, no. 1, p. 3, 2010.CrossRefPubMedPubMedCentral N. Sharma, L. M. Aggarwal, et al., “Automated medical image segmentation techniques,” Journal of medical physics, vol. 35, no. 1, p. 3, 2010.CrossRefPubMedPubMedCentral
11.
go back to reference R. A. Rensink, “The dynamic representation of scenes,” Visual cognition, vol. 7, no. 1-3, pp. 17–42, 2000.CrossRef R. A. Rensink, “The dynamic representation of scenes,” Visual cognition, vol. 7, no. 1-3, pp. 17–42, 2000.CrossRef
12.
go back to reference A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
13.
go back to reference A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:​2010.​11929, 2020.
14.
go back to reference T. Messay, R. C. Hardie, and S. K. Rogers, “A new computationally efficient cad system for pulmonary nodule detection in ct imagery,” Medical image analysis, vol. 14, no. 3, pp. 390–406, 2010.CrossRefPubMed T. Messay, R. C. Hardie, and S. K. Rogers, “A new computationally efficient cad system for pulmonary nodule detection in ct imagery,” Medical image analysis, vol. 14, no. 3, pp. 390–406, 2010.CrossRefPubMed
15.
go back to reference C. Jacobs, E. M. Van Rikxoort, T. Twellmann, E. T. Scholten, P. A. De Jong, J.-M. Kuhnigk, M. Oudkerk, H. J. De Koning, M. Prokop, C. Schaefer-Prokop, et al., “Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images,” Medical image analysis, vol. 18, no. 2, pp. 374–384, 2014.CrossRefPubMed C. Jacobs, E. M. Van Rikxoort, T. Twellmann, E. T. Scholten, P. A. De Jong, J.-M. Kuhnigk, M. Oudkerk, H. J. De Koning, M. Prokop, C. Schaefer-Prokop, et al., “Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images,” Medical image analysis, vol. 18, no. 2, pp. 374–384, 2014.CrossRefPubMed
16.
go back to reference E. Lopez Torres, E. Fiorina, F. Pennazio, C. Peroni, M. Saletta, N. Camarlinghi, M. Fantacci, and P. Cerello, “Large scale validation of the m5l lung cad on heterogeneous ct datasets,” Medical physics, vol. 42, no. 4, pp. 1477–1489, 2015. E. Lopez Torres, E. Fiorina, F. Pennazio, C. Peroni, M. Saletta, N. Camarlinghi, M. Fantacci, and P. Cerello, “Large scale validation of the m5l lung cad on heterogeneous ct datasets,” Medical physics, vol. 42, no. 4, pp. 1477–1489, 2015.
17.
go back to reference H. Law and J. Deng, “Cornernet: Detecting objects as paired keypoints,” in Proceedings of the European conference on computer vision (ECCV), pp. 734–750, 2018. H. Law and J. Deng, “Cornernet: Detecting objects as paired keypoints,” in Proceedings of the European conference on computer vision (ECCV), pp. 734–750, 2018.
18.
go back to reference X. Lu, B. Li, Y. Yue, Q. Li, and J. Yan, “Grid r-cnn,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7363–7372, 2019. X. Lu, B. Li, Y. Yue, Q. Li, and J. Yan, “Grid r-cnn,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7363–7372, 2019.
19.
go back to reference X. Zhou, J. Zhuo, and P. Krahenbuhl, “Bottom-up object detection by grouping extreme and center points,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 850–859, 2019. X. Zhou, J. Zhuo, and P. Krahenbuhl, “Bottom-up object detection by grouping extreme and center points,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 850–859, 2019.
20.
go back to reference L. Huang, Y. Yang, Y. Deng, and Y. Yu, “Densebox: Unifying landmark localization with end to end object detection,” arXiv preprint arXiv:1509.04874, 2015. L. Huang, Y. Yang, Y. Deng, and Y. Yu, “Densebox: Unifying landmark localization with end to end object detection,” arXiv preprint arXiv:​1509.​04874, 2015.
21.
go back to reference J. Wang, K. Chen, S. Yang, C. C. Loy, and D. Lin, “Region proposal by guided anchoring,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974, 2019. J. Wang, K. Chen, S. Yang, C. C. Loy, and D. Lin, “Region proposal by guided anchoring,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2965–2974, 2019.
22.
go back to reference J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141, 2018. J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141, 2018.
23.
go back to reference L. Gong, S. Jiang, Z. Yang, G. Zhang, and L. Wang, “Automated pulmonary nodule detection in ct images using 3d deep squeeze-and-excitation networks,” International journal of computer assisted radiology and surgery, vol. 14, pp. 1969–1979, 2019.CrossRefPubMed L. Gong, S. Jiang, Z. Yang, G. Zhang, and L. Wang, “Automated pulmonary nodule detection in ct images using 3d deep squeeze-and-excitation networks,” International journal of computer assisted radiology and surgery, vol. 14, pp. 1969–1979, 2019.CrossRefPubMed
24.
go back to reference Y. Li and Y. Fan, “Deepseed: 3d squeeze-and-excitation encoder-decoder convolutional neural networks for pulmonary nodule detection,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1866–1869, IEEE, 2020. Y. Li and Y. Fan, “Deepseed: 3d squeeze-and-excitation encoder-decoder convolutional neural networks for pulmonary nodule detection,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1866–1869, IEEE, 2020.
25.
go back to reference Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “Eca-net: Efficient channel attention for deep convolutional neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11534–11542, 2020. Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “Eca-net: Efficient channel attention for deep convolutional neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11534–11542, 2020.
26.
go back to reference Z. Guo, L. Zhao, J. Yuan, and H. Yu, “Msanet: Multiscale aggregation network integrating spatial and channel information for lung nodule detection,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 6, pp. 2547–2558, 2021.CrossRef Z. Guo, L. Zhao, J. Yuan, and H. Yu, “Msanet: Multiscale aggregation network integrating spatial and channel information for lung nodule detection,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 6, pp. 2547–2558, 2021.CrossRef
27.
go back to reference S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European conference on computer vision (ECCV), pp. 3–19, 2018. S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European conference on computer vision (ECCV), pp. 3–19, 2018.
28.
go back to reference L. Sun, Z. Wang, H. Pu, G. Yuan, L. Guo, T. Pu, and Z. Peng, “Attention-embedded complementary-stream cnn for false positive reduction in pulmonary nodule detection,” Computers in Biology and Medicine, vol. 133, p. 104357, 2021.CrossRefPubMed L. Sun, Z. Wang, H. Pu, G. Yuan, L. Guo, T. Pu, and Z. Peng, “Attention-embedded complementary-stream cnn for false positive reduction in pulmonary nodule detection,” Computers in Biology and Medicine, vol. 133, p. 104357, 2021.CrossRefPubMed
29.
go back to reference C. Wen, M. Hong, X. Yang, and J. Jia, “Pulmonary nodule detection based on convolutional block attention module,” in 2019 Chinese Control Conference (CCC), pp. 8583–8587, IEEE, 2019. C. Wen, M. Hong, X. Yang, and J. Jia, “Pulmonary nodule detection based on convolutional block attention module,” in 2019 Chinese Control Conference (CCC), pp. 8583–8587, IEEE, 2019.
30.
go back to reference N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, pp. 213–229, Springer, 2020. N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, pp. 213–229, Springer, 2020.
31.
go back to reference X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable detr: Deformable transformers for end-to-end object detection,” arXiv preprint arXiv:2010.04159, 2020. X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable detr: Deformable transformers for end-to-end object detection,” arXiv preprint arXiv:​2010.​04159, 2020.
32.
go back to reference I. Misra, R. Girdhar, and A. Joulin, “An end-to-end transformer model for 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2906–2917, 2021. I. Misra, R. Girdhar, and A. Joulin, “An end-to-end transformer model for 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2906–2917, 2021.
33.
go back to reference Z. Dai, B. Cai, Y. Lin, and J. Chen, “Up-detr: Unsupervised pre-training for object detection with transformers,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1601–1610, 2021. Z. Dai, B. Cai, Y. Lin, and J. Chen, “Up-detr: Unsupervised pre-training for object detection with transformers,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1601–1610, 2021.
34.
go back to reference J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A. L. Yuille, and Y. Zhou, “Transunet: Transformers make strong encoders for medical image segmentation,” arXiv preprint arXiv:2102.04306, 2021. J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A. L. Yuille, and Y. Zhou, “Transunet: Transformers make strong encoders for medical image segmentation,” arXiv preprint arXiv:​2102.​04306, 2021.
35.
go back to reference W. Wang, C. Chen, M. Ding, H. Yu, S. Zha, and J. Li, “Transbts: Multimodal brain tumor segmentation using transformer,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119, Springer, 2021. W. Wang, C. Chen, M. Ding, H. Yu, S. Zha, and J. Li, “Transbts: Multimodal brain tumor segmentation using transformer,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, pp. 109–119, Springer, 2021.
36.
go back to reference H. Jiang, P. Zhang, C. Che, B. Jin, et al., “Rdfnet: A fast caries detection method incorporating transformer mechanism,” Computational and Mathematical Methods in Medicine, vol. 2021, 2021. H. Jiang, P. Zhang, C. Che, B. Jin, et al., “Rdfnet: A fast caries detection method incorporating transformer mechanism,” Computational and Mathematical Methods in Medicine, vol. 2021, 2021.
37.
go back to reference X. Ma, G. Luo, W. Wang, and K. Wang, “Transformer network for significant stenosis detection in ccta of coronary arteries,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24, pp. 516–525, Springer, 2021. X. Ma, G. Luo, W. Wang, and K. Wang, “Transformer network for significant stenosis detection in ccta of coronary arteries,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24, pp. 516–525, Springer, 2021.
38.
go back to reference H. Li, L. Chen, H. Han, and S. Kevin Zhou, “Satr: Slice attention with transformer for universal lesion detection,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part III, pp. 163–174, Springer, 2022. H. Li, L. Chen, H. Han, and S. Kevin Zhou, “Satr: Slice attention with transformer for universal lesion detection,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part III, pp. 163–174, Springer, 2022.
39.
go back to reference A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. Van Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. Van Ginneken, “Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks,” IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1160–1169, 2016.CrossRefPubMed A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. Van Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. Van Ginneken, “Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks,” IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1160–1169, 2016.CrossRefPubMed
40.
go back to reference J. Mei, M.-M. Cheng, G. Xu, L.-R. Wan, and H. Zhang, “Sanet: A slice-aware network for pulmonary nodule detection,” IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 8, pp. 4374–4387, 2021. J. Mei, M.-M. Cheng, G. Xu, L.-R. Wan, and H. Zhang, “Sanet: A slice-aware network for pulmonary nodule detection,” IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 8, pp. 4374–4387, 2021.
41.
go back to reference W. Zhu, C. Liu, W. Fan, and X. Xie, “Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification,” in 2018 IEEE winter conference on applications of computer vision (WACV), pp. 673–681, IEEE, 2018. W. Zhu, C. Liu, W. Fan, and X. Xie, “Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification,” in 2018 IEEE winter conference on applications of computer vision (WACV), pp. 673–681, IEEE, 2018.
42.
go back to reference H. Tang, C. Zhang, and X. Xie, “Nodulenet: Decoupled false positive reduction for pulmonary nodule detection and segmentation,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI 22, pp. 266–274, Springer, 2019. H. Tang, C. Zhang, and X. Xie, “Nodulenet: Decoupled false positive reduction for pulmonary nodule detection and segmentation,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI 22, pp. 266–274, Springer, 2019.
43.
go back to reference T. Song, J. Chen, X. Luo, Y. Huang, X. Liu, N. Huang, Y. Chen, Z. Ye, H. Sheng, S. Zhang, et al., “Cpm-net: A 3d center-points matching network for pulmonary nodule detection in ct scans,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 550–559, Springer, 2020. T. Song, J. Chen, X. Luo, Y. Huang, X. Liu, N. Huang, Y. Chen, Z. Ye, H. Sheng, S. Zhang, et al., “Cpm-net: A 3d center-points matching network for pulmonary nodule detection in ct scans,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 550–559, Springer, 2020.
44.
go back to reference X. Luo, T. Song, G. Wang, J. Chen, Y. Chen, K. Li, D. N. Metaxas, and S. Zhang, “Scpm-net: An anchor-free 3d lung nodule detection network using sphere representation and center points matching,” Medical Image Analysis, vol. 75, p. 102287, 2022.CrossRefPubMed X. Luo, T. Song, G. Wang, J. Chen, Y. Chen, K. Li, D. N. Metaxas, and S. Zhang, “Scpm-net: An anchor-free 3d lung nodule detection network using sphere representation and center points matching,” Medical Image Analysis, vol. 75, p. 102287, 2022.CrossRefPubMed
45.
go back to reference I. W. Harsono, S. Liawatimena, and T. W. Cenggoro, “Lung nodule detection and classification from thorax ct-scan using retinanet with transfer learning,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 3, pp. 567–577, 2022.CrossRef I. W. Harsono, S. Liawatimena, and T. W. Cenggoro, “Lung nodule detection and classification from thorax ct-scan using retinanet with transfer learning,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 3, pp. 567–577, 2022.CrossRef
Metadata
Title
TiCNet: Transformer in Convolutional Neural Network for Pulmonary Nodule Detection on CT Images
Authors
Ling Ma
Gen Li
Xingyu Feng
Qiliang Fan
Lizhi Liu
Publication date
10-01-2024
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 1/2024
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-023-00904-y

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