Abstract
Lung cancer cell detection serves as an important step in the automation of cell-based lung cancer diagnosis. In this paper, we propose a robust and efficient lung cancer cell detection method based on the accelerated Deep Convolution Neural Network framework(DCNN). The efficiency of the proposed method is demonstrated in two aspects: (1) We adopt a training strategy, learning the DCNN model parameters from only weakly annotated cell information (one click near the nuclei location). This technique significantly reduces the manual annotation cost and the training time. (2) We introduce a novel DCNN forward acceleration technique into our method, which speeds up the cell detection process several hundred times than the conventional sliding-window based DCNN. In the reported experiments, state-of-the-art accuracy and the impressive efficiency are demonstrated in the lung cancer histopathological image dataset.
This work was partially supported by U.S. NSF IIS-1423056, CMMI-1434401, CNS-1405985.
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References
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Bernardis, E., Stella, X.Y.: Pop out many small structures from a very large microscopic image. Med. Image Anal. 15(5), 690–707 (2011)
Nath, S.K., Palaniappan, K., Bunyak, F.: Cell segmentation using coupled level sets and graph-vertex coloring. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 101–108. Springer, Heidelberg (2006)
Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to detect cells using non-overlapping extremal regions. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 348–356. Springer, Heidelberg (2012)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition (2014)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Li, R., Zhang, W., Suk, H.-I., Wang, L., Li, J., Shen, D., Ji, S.: Deep learning based imaging data completion for improved brain disease diagnosis. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part III. LNCS, vol. 8675, pp. 305–312. Springer, Heidelberg (2014)
Li, H., Zhao, R., Wang, X.: Highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification (2014). arXiv preprint arXiv:1412.4526
Team, N.L.S.T.R., et al.: The national lung screening trial: overview and study design. Radiology 258, 243–253 (2011)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding (2014). arXiv preprint arXiv:1408.5093
Huang, J., Huang, X., Metaxas, D.: Simultaneous image transformation and sparse representation recovery. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
Huang, J., Huang, X., Metaxas, D.: Learning with dynamic group sparsity. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 64–71. IEEE (2009)
Huang, J., Zhang, S., Li, H., Metaxas, D.: Composite splitting algorithms for convex optimization. Comput. Vision Image Underst. 115(12), 1610–1622 (2011)
Acknowledgments
The authors would like to thank NVIDIA for GPU donation and the National Cancer Institute for access to NCI’s data collected by the National Lung Screening Trial. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI.
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Xu, Z., Huang, J. (2015). Efficient Lung Cancer Cell Detection with Deep Convolution Neural Network. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2015. Lecture Notes in Computer Science(), vol 9467. Springer, Cham. https://doi.org/10.1007/978-3-319-28194-0_10
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DOI: https://doi.org/10.1007/978-3-319-28194-0_10
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