Abstract
In the early diagnosis of lung cancer, an important step is classifying malignancy/benignity for each lung nodule. For this classification, the nodule’s features (e.g., shape, margin) have traditionally been the main focus. Recently, the contextual features attract increasing attention, due to the complementary information they provide. Clinically, such contextual features refer to the features of nodule’s surrounding structures, such that (together with nodule’s features) they can expose discriminate patterns for the malignant/benign, such as vascular convergence and fissural attachment. To leverage such contextual features, we propose a Context Attention Network (CA-Net) which extracts both nodule’s and contextual features and then effectively fuses them during malignancy/benignity classification. To accurately identify the contextual features that contain structures distorted/attached by the nodule, we take the nodule’s features as a reference via an attention mechanism. Further, we propose a feature fusion module that can adaptively adjust the weights of nodule’s and contextual features across nodules. The utility of our proposed method is demonstrated by a noticeable margin over the 1st place on Data Science Bowl 2017 dataset in Kaggle’s competition.
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Notes
- 1.
The \(a \circ b\) denotes the element-wise product between a, b.
- 2.
LIDC-IDRI [12] is not considered for the lack of biopsy confirmed lung cancer labels.
- 3.
Official evaluation metric of the competition, lower value means better performance.
References
Data science bowl 2017 (2017). https://www.kaggle.com/c/data-science-bowl-2017
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:abs/1706.05587 (2017)
Chen, S., et al.: Automatic scoring of multiple semantic attributes with multi-task feature leverage: a study on pulmonary nodules in CT images. IEEE Trans. Med. Imaging 36(3), 802–814 (2017). https://doi.org/10.1109/TMI.2016.2629462
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_23
Hu, H., Wang, Q., Tang, H., Xiong, L., Lin, Q.: Multi-slice computed tomography characteristics of solitary pulmonary ground-glass nodules: differences between malignant and benign. Thoracic Cancer 7(1), 80–87 (2016). https://doi.org/10.1111/1759-7714.12280
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018). https://doi.org/10.1109/CVPR.2018.00745
Lei, Y., Tian, Y., Shan, H., Zhang, J., Wang, G., Kalra, M.K.: Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping. Med. Image Anal. 60, 101628 (2020). https://doi.org/10.1016/j.media.2019.101628
Liao, F., Liang, M., Li, Z., Hu, X., Song, S.: Evaluate the malignancy of pulmonary nodules using the 3-d deep leaky noisy-or network. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3484–3495 (2019). https://doi.org/10.1109/TNNLS.2019.2892409
Liu, L., Dou, Q., Chen, H., Qin, J., Heng, P.A.: Multi-task deep model with margin ranking loss for lung nodule analysis. IEEE Trans. Med. Imaging 39(3), 718–728 (2020). https://doi.org/10.1109/TMI.2019.2934577
Liu, X., Hou, F., Qin, H., Hao, A.: Multi-view multi-scale CNNs for lung nodule type classification from CT images. Pattern Recogn. 77, 262–275 (2018). https://doi.org/10.1016/j.patcog.2017.12.022
McNitt-Gray, M.F., et al.: The lung image database consortium (LIDC) data collection process for nodule detection and annotation. Acad. Radiol. 14(12), 1464–1474 (2007). https://doi.org/10.1016/j.acra.2007.07.021
Ozdemir, O., Russell, R.L., Berlin, A.A.: A 3D probabilistic deep learning system for detection and diagnosis of lung cancer using low-dose CT scans. IEEE Trans. Med. Imaging 39(5), 1419–1429 (2020). https://doi.org/10.1109/TMI.2019.2947595
Qiang, J., et al.: The relationship between solitary pulmonary nodules and bronchi: multi-slice CT-pathological correlation. Clin. Radiol. 59(12), 1121–1127 (2004). https://doi.org/10.1016/j.crad.2004.02.018
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618–626 (2017). https://doi.org/10.1109/ICCV.2017.74
Shen, W., et al.: Learning from experts: developing transferable deep features for patient-level lung cancer prediction. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 124–131. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_15
Shen, W., et al.: Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recogn. 61, 663–673 (2017). https://doi.org/10.1016/j.patcog.2016.05.029
Xie, Y., et al.: Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Trans. Med. Imaging 38(4), 991–1004 (2019). https://doi.org/10.1109/TMI.2018.2876510
Xu, D.M., et al.: Smooth or attached solid indeterminate nodules detected at baseline CT screening in the Nelson study: cancer risk during 1 year of follow-up. Radiology 250(1), 264–272 (2009). https://doi.org/10.1148/radiol.2493070847
Zhang, H., Xue, J., Dana, K.: Deep TEN: texture encoding network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2896–2905 (2017). https://doi.org/10.1109/CVPR.2017.309
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6230–6239 (2017). https://doi.org/10.1109/CVPR.2017.660
Zwirewich, C.V., Vedal, S., Miller, R.R., Müller, N.L.: Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation. Radiology 179(2), 469–476 (1991). https://doi.org/10.1148/radiology.179.2.2014294
Acknowledgements
This work was supported by MOST-2018AAA0102004, NSFC-61625201, and the Beijing Municipal Science and Technology Planning Project (Grant No. Z201100005620008).
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Liu, M., Zhang, F., Sun, X., Yu, Y., Wang, Y. (2021). CA-Net: Leveraging Contextual Features for Lung Cancer Prediction. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_3
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