Abstract
Pulmonary nodule differentiation is one of the most challenge tasks of computer-aided diagnosis(CADx). Both texture method and shape estimation approaches previously presented could provide good performance to some extent in the literature. However, no matter 2D or 3D textures extracted, they just tend to observe characteristics of the pulmonary nodules from a statistical perspective according to local features’ change, which hints they are helpless to work as global as the human who always be aware of the characteristics of given target as a combination of local features and global features, thus they have certain limitations. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN) and previously contributions provided by texture features, we here presented a hybrid method for better to complete the differentiation task. It can be observed that our proposed multi-channel CNN model has a better discrimination in capacity according to the projection of distributions of extracted features and achieved a new record with AUC 97.04 on LIDC-IDRI database.
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References
Anirudh, R., Thiagarajan, J.J., Bremer, P., Kim, H.: Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data. In: Proceedings of SPIE, p. 978532 (2016)
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., Mougiakakou, S.: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016)
Armato, S.G., Mclennan, G., Bidaut, L., Mcnittgray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI) a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection, vol. 1, pp. 886–893 (2005)
Der Walt, S.V., Schonberger, J.L., Nuneziglesias, J., Boulogne, F., Warner, J.D., Yager, N., Gouillart, E., Yu, T.: Scikit-image: image processing in python. PeerJ 2, e453 (2014)
Gutierrez, D.: Cancer Facts and Figures. American Family Physician (2015)
Han, F., Wang, H., Song, B., Zhang, G., Lu, H., Moore, W., Liang, Z., Zhao, H.: Efficient 3d texture feature extraction from CT images for computer-aided diagnosis of pulmonary nodules. In: Proceedings of SPIE (2014)
Hinton, G.E.: Visualizing high-dimensional data using T-SNE. Vigiliae Christianae (2008)
Kumar, D., Wong, A., Clausi, D.A.: Lung nodule classification using deep features in CT images, pp. 133–138 (2015)
Ludwig, O., Delgado, D., Goncalves, V., Nunes, U.: Trainable classifier-fusion schemes: an application to pedestrian detection. In: International IEEE Conference on Intelligent Transportation Systems, pp. 1–6 (2009)
Macmahon, H., Austin, J.H.M., Gamsu, G., Herold, C.J., Jett, J.R., Naidich, D.P., Patz, E.F., Swensen, S.J.: Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the fleischner society. Radiology 237(2), 395–400 (2005)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)
Shen, W., Zhou, M., Yang, F., Yu, D., Dong, D., Yang, C., Zang, Y., Tian, J.: Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognit. 61, 663–673 (2017)
Shin, H., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D.J., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Wang, X., Han, T.X., Yan, S.: An hog-LBP human detector with partial occlusion handling, pp. 32–39 (2009)
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Zhao, T. et al. (2017). A Hybrid CNN Feature Model for Pulmonary Nodule Differentiation Task. In: Cardoso, M., et al. Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound. BIVPCS POCUS 2017 2017. Lecture Notes in Computer Science(), vol 10549. Springer, Cham. https://doi.org/10.1007/978-3-319-67552-7_3
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DOI: https://doi.org/10.1007/978-3-319-67552-7_3
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