Abstract
This paper presents the approach for lung adenocarcinoma diagnosis, using deep convolutional neural networks (CNN) to learn the features from the tissue pathology images. Our multi-stage procedure can detect the lung cancer of adenocarcinoma, in which the preprocessing consists of image enhancement and class imbalance treatment. Then Gradient-weighted Class Activation Mapping (Grad-CAM) and Guided-Backpropagation visualization techniques are employed to produce the visual explanations for decisions from our CNN model. Learned features and details for the specific areas have been generated through the model. Data is collected from 22 different patients with 270 lesion images and 24 normal ones. Experimental result on this data set has achieved F1-score with 0.963. Moreover, the study is not only to pursue precise classification on the tissue pathology images of lung adenocarcinoma, but also learn the specific areas in images which should be more concerned by doctors.
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References
Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. (2017)
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A., van Ginneken, B., Sanchez, C.I.: A survey on deep learning in medical image analysis. arxiv preprint arXiv:1702.05747 (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arxiv preprint arXiv:1409.1556 (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_53
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)
Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D.: Gradcam: Why did you say that? visual explanations from deep networks via gradient based localization. arxiv preprint. arXiv:1610.02391 (2016)
Zuiderveld, K.: Contrast Limited Adaptive Histogram Equalization. Academic Press Professional Inc., Cambridge (1994)
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. arxiv preprint arXiv:1412.6806 (2014)
Kawahara, J., Hamarneh, G.: Multi-resolution-Tract CNN with hybrid pretrained and skin-lesion trained layers. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, H.-I. (eds.) MLMI 2016. LNCS, vol. 10019, pp. 164–171. Springer, Cham (2016). doi:10.1007/978-3-319-47157-0_20
Shen, W., Zhou, M., Yang, F., Yang, C., Tian, J.: Multi-scale convolutional neural networks for lung nodule classification. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 588–599. Springer, Cham (2015). doi:10.1007/978-3-319-19992-4_46
de Vos, B.D., Wolterink, J.M., de Jong, P.A., Viergever, M.A., Isgum, I.: 2D image classification for 3D anatomy localization: employing deep convolutional neural networks. In: Medical Imaging 2016: Image Processing, vol. 9784 (2016)
Yang, D., Zhang, S., Yan, Z., Tan, C., Li, K., Metaxas, D.: Automated anatomical landmark detection ondistal femur surface using convolutional neural network. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 17–21. IEEE (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). doi:10.1007/978-3-319-24574-4_28
Wells, W.M., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1(1), 35–51 (1996)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 61672276) and Natural Science Foundation of Jiangsu, China (BK20161406).
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He, J., Shang, L., Ji, H., Zhang, X. (2017). Deep Learning Features for Lung Adenocarcinoma Classification with Tissue Pathology Images. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_79
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