Abstract
Deep Learning based lung nodule detection is rapidly growing. It is one of the most challenging tasks to increase the true positive while decreasing the false positive. In this paper, we propose a novel attention 3D fully Convolutional Neural Network for lung nodule detection to tackle this problem. It performs automatic suspect localization by a new channel-spatial attention U-Network with Squeeze and Excitation Blocks (U-SENet) for candidate nodules segmentation, following by a Fully Convolutional C3D (FC-C3D) network to reduce the false positives. The weights of spatial units and channels for U-SENet can be adjusted to focus on the regions related to the lung nodules. These candidate nodules are input to FC-C3D network, where the convolutional layers are re-placed by the fully connected layers, so that the size of the input feature map is no longer limited. In addition, voting fusion and weighted average fusion are adopted to improve the efficiency of the network. The experiments we implement demonstrate our model outperforms the other methods in the effectiveness, with the sensitivity up to 93.3\(\%\).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Siegel, R.L., Miller, K.D., Fuchs, H.E., Jemal, A.: Cancer statistics, 2022. CA Cancer J. Clin. (2022). https://doi.org/10.3322/caac.21708
American Cancer Society.https://www.cancer.org/cancer/lung-cancer/about/key-statistics.html. Accessed 1 June 2022
Arindra, A., Setio, A., Traverso, A.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med. Image Anal. 42, 1–13 (2017) https://doi.org/10.1016/j.media.2017.06.015
Arindra, A., Setio, A., Ciompi, F., et al.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE 35(5), 1160–1169 (2016). https://doi.org/10.1109/TMI.2016.2536809
Qu, K., Chai, X., Liu, T., Zhang, Y., Leng, B., Xiong, Z.: Computer-aided diagnosis in chest radiography with deep multi-instance learning. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds.) ICONIP 2017. LNCS, vol. 10637, pp. 723–731. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70093-9_77
Cao, G.T., et al.: 3D convolutional neural networks fusion model for lung nodule detection on clinical CT scans. In: IEEE International Conference on Bioinformatics and Bio-medicine. Madrid, Spain, pp. 973–978 (2018). https://doi.org/10.1109/BIBM.2018.8621468
Yang, F., Zhang, H., Tao, S., et al.: Graph representation learning via simple jumping knowledge networks. Appl. Intell. 52, 11324–11342 (2018). https://doi.org/10.1007/s10489-021-02889-z
Chen, W.Q., et al.: Cancer incidence and mortality in China. Chin. J. Cancer Res. 27(1), 1004–0242 (2018). https://doi.org/10.21147/J.ISSN.1000-9604.2018.01.01
Song, J., et al.: Human action recognition with 3D convolution skip-connections and RNNs. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11301, pp. 319–331. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04167-0_29
Wu, C., Liu, X., Li, S., Long, C.: Coordinate attention residual deformable U-net for vessel segmentation. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. LNCS, vol. 13110, pp. 345–356. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92238-2_29
Cheng, D.C., et al.: SeNet: structured edge network for sea-land segmentation. IEEE Geosci. Remote Sens. Lett. 14(2), 247–251 (2017). https://doi.org/10.1109/LGRS.2016.2637439
Ding J., Li, A., Hu, Z.Q., Wang, L.W.: Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In: Medical Image Computing and Computer-Assisted Intervention, Quebec City, Quebec, Canada, pp. 559–567 (2017). https://doi.org/10.1007/978-3-319-66179-7_64
AbdelMaksoud, E., Barakat, S., Elmogy, M.: A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique. Med. Biol. Eng. Comput. 60, 2015–2038 (2022). https://doi.org/10.1007/s11517-022-02564-6
Hao, X.Y., Xiong, J.F., Xue, X.D., et al.: 3D U-net with dual attention mechanism for lung tumor segmentation. J. Image Graph. 25(10), 2119–2127 (2020). https://doi.org/10.11834/jig.200282
Tan, J.X., Huo, Y.M., et al.: Expert knowledge-infused deep learning for automatic lung nodule detection. J. Xray Sci. Technol. 27(1), 17–35 (2018). https://doi.org/10.3233/XST-180426
Liu, J., Cao, L., Akin, O., Tian, Y.: 3DFPN-HS\(^2\): 3D feature pyramid network based high sensitivity and specificity pulmonary nodule detection. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 513–521. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_57
Murphy, K., et al.: A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. 13(5), 757–770 (2009). https://doi.org/10.1016/J.MEDIA.2009.07.001
Srivastava, N., Hinton, G., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2013)
Wang, D., Zhang, Y., Zhang, K.X., et al.: FocalMix: semi-supervised learning for 3D medical image detection. In: CVPR: Computer Vision and Pattern Recognition. Seattle, WA, USA, pp. 3951–3960 (2020). https://doi.org/10.1109/CVPR42600.2020.00401
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cao, G., Yang, Q., Zheng, B., Hou, K., Zhang, J. (2023). Attention 3D Fully Convolutional Neural Network for False Positive Reduction of Lung Nodule Detection. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. Springer, Singapore. https://doi.org/10.1007/978-981-99-1645-0_28
Download citation
DOI: https://doi.org/10.1007/978-981-99-1645-0_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1644-3
Online ISBN: 978-981-99-1645-0
eBook Packages: Computer ScienceComputer Science (R0)