Three-Dimensional Multifaceted Attention Encoder–Decoder Networks for Pulmonary Nodule Detection
Source abstract
Abstract
Lung cancer is one of the most dangerous cancers in the world, and its early clinical manifestation is malignant nodules in the lungs, so nodule detection in the lungs can provide the basis for the prevention and treatment of lung cancer. In recent years, the development of neural networks has provided a new paradigm for creating computer-aided systems for pulmonary nodule detection. Currently, the mainstream pulmonary nodule detection models are based on convolutional neural networks (CNN); however, as the output of a CNN is based on a fixed-size convolutional kernel, it can lead to a model that cannot establish an effective long-range dependence and can only model local features of CT images. The self-attention block in the traditional transformer structures, although able to establish long-range dependence, are as ineffective as CNN structures in dealing with irregular lesions of nodules. To overcome these problems, this paper combines the self-attention block with the learnable regional attention block to form the multifaceted attention block, which enables the model to establish a more effective long-term dependence based on the characteristics of pulmonary nodules. And the multifaceted attention block is intermingled with the encoder–decoder structure in the CNN to propose the 3D multifaceted attention encoder–decoder network (MAED), which is able to model CT images locally while establishing effective long-term dependencies. In addition, we design a multiscale module to extract the features of pulmonary nodules at different scales and use a focal loss function to reduce the false alarm rate. We evaluated the proposed model on the large-scale public dataset LUNA16, with an average sensitivity of 89.1% across the seven predefined FPs/scan criteria. The experimental results show that the MAED model is able to simultaneously achieve efficient detection of pulmonary nodules and filtering of false positive nodules.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Lung Cancer Diagnosis and Treatment · Radiomics and Machine Learning in Medical Imaging · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction, Shenyang 110168, China
References (33)
Show source-provided references
1. Lung cancer screening, version 3.2018, NCCN clinical practice guidelines in oncology10.6004/jnccn.2018.0020 ↗
2. Lung cancer screening with low-dose helical CT: Results from the National Lung Screening Trial (NLST)10.1258/jms.2011.011055 ↗
3. Ding, J., Li, A., Hu, Z., and Wang, L. (2017, January 11–13). Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. Proceedings of the Medical Image Computing and Computer Assisted Intervention-MICCAI 2017: 20th International Conference, Quebec City, QC, Canada. Proceedings, Part III 20.10.1007/978-3-319-66179-7_64 ↗
4. Wani, S.U.D., Khan, N.A., Thakur, G., Gautam, S.P., Ali, M., Alam, P., Alshehri, S., Ghoneim, M.M., and Shakeel, F. (2022). Utilization of artificial intelligence in disease prevention: Diagnosis, treatment, and implications for the healthcare workforce. Healthcare, 10.10.3390/healthcare10040608 ↗
5. Li, R., Xiao, C., Huang, Y., Hassan, H., and Huang, B. (2022). Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: A review. Diagnostics, 12.10.3390/diagnostics12020298 ↗
6. Pulmonary Nodule Detection Based on Multiscale Feature Fusion10.1155/2022/8903037 ↗
7. Pulmonary nodules detection based on multi-scale attention networks10.1038/s41598-022-05372-y ↗
8. Peng, H., Sun, H., and Guo, Y. (2021). 3D multi-scale deep convolutional neural networks for pulmonary nodule detection. PLoS ONE, 16.10.1371/journal.pone.0244406 ↗
9. SCPM-Net: An anchor-free 3D lung nodule detection network using sphere representation and center points matching10.1016/j.media.2021.102287 ↗
10. Li, Y., and Fan, Y. (2020, January 3–7). DeepSEED: 3D squeeze-and-excitation encoder-decoder convolutional neural networks for pulmonary nodule detection. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA.10.1109/ISBI45749.2020.9098317 ↗
11. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30.
12. Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. arXiv.
13. MSM-ViT: A multi-scale MobileViT for pulmonary nodule classification using CT images
14. Matsoukas, C., Haslum, J.F., Söderberg, M., and Smith, K. (2021). Is it time to replace cnns with transformers for medical images?. arXiv.
15. A vision transformer for emphysema classification using CT images10.1088/1361-6560/ac3dc8 ↗
16. Mehta, S., and Rastegari, M. (2021). Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. arXiv.
17. Lin, H., Ma, Z., Ji, R., Wang, Y., and Hong, X. (2022, January 18–24). Boosting crowd counting via multifaceted attention. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.10.1109/CVPR52688.2022.01901 ↗
18. Focal loss for dense object detection10.1109/TPAMI.2018.2858826 ↗
19. Khosravan, N., and Bagci, U. (2018, January 16–20). S4ND: Single-shot single-scale lung nodule detection. Proceedings of the Medical Image Computing and Computer Assisted Intervention-MICCAI 2018: 21st International Conference, Granada, Spain. Proceedings, Part II 11.10.1007/978-3-030-00934-2_88 ↗
20. One-stage pulmonary nodule detection using 3-D DCNN with feature fusion and attention mechanism in CT image10.1016/j.cmpb.2022.106786 ↗
21. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv.
22. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv.
23. Yang, J., Deng, H., Huang, X., Ni, B., and Xu, Y. (2020, January 3–7). Relational learning between multiple pulmonary nodules via deep set attention transformers. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA.10.1109/ISBI45749.2020.9098722 ↗
24. Beltagy, I., Peters, M.E., and Cohan, A. (2020). Longformer: The long-document transformer. arXiv.
25. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11–17). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada.10.1109/ICCV48922.2021.00986 ↗
26. Li, J., Wang, W., Chen, C., Zhang, T., Zha, S., Yu, H., and Wang, J. (2022). Transbtsv2: Wider instead of deeper transformer for medical image segmentation. arXiv.
27. Zhang, P., Dai, X., Yang, J., Xiao, B., Yuan, L., Zhang, L., and Gao, J. (2021, January 11–17). Multi-scale vision longformer: A new vision transformer for high-resolution image encoding. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada.10.1109/ICCV48922.2021.00299 ↗
28. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge10.1016/j.media.2017.06.015 ↗
29. Dou, Q., Chen, H., Jin, Y., Lin, H., Qin, J., and Heng, P.A. (2017, January 11–13). Automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid-loss residual learning. Proceedings of the Medical Image Computing and Computer Assisted Intervention-MICCAI 2017: 20th International Conference, Quebec City, QC, Canada. Proceedings, Part III 20.10.1007/978-3-319-66179-7_72 ↗
30. Zhu, W., Liu, C., Fan, W., and Xie, X. (2018, January 12–15). Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.10.1109/WACV.2018.00079 ↗
31. Tang, H., Zhang, C., and Xie, X. (2019, January 13–17). Nodulenet: Decoupled false positive reduction for pulmonary nodule detection and segmentation. Proceedings of the Medical Image Computing and Computer Assisted Intervention-MICCAI 2019: 22nd International Conference, Shenzhen, China. Proceedings, Part VI 22.10.1007/978-3-030-32226-7_30 ↗
32. Wang, B., Qi, G., Tang, S., Zhang, L., Deng, L., and Zhang, Y. (2018, January 16–20). Automated pulmonary nodule detection: High sensitivity with few candidates. Proceedings of the Medical Image Computing and Computer Assisted Intervention-MICCAI 2018: 21st International Conference, Granada, Spain. Proceedings, Part II.10.1007/978-3-030-00934-2_84 ↗
33. Song, T., Chen, J., Luo, X., Huang, Y., Liu, X., Huang, N., Chen, Y., Ye, Z., Sheng, H., and Zhang, S. (2020, January 4–8). CPM-Net: A 3D center-points matching network for pulmonary nodule detection in CT scans. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention 2020, Lima, Peru.10.1007/978-3-030-59725-2_53 ↗