Abstract
As lung cancer is one of the most frequent and serious disease causing death for both men and women, early diagnosis and differentiation of lung cancers is clinically important. Lung cancer cell detection is the most basic step among the Computer-aided histopathology lung image analysis applications. We proposed an automatic lung cancer cell detection method based on deep convolutional neural network. In this method, we need only the weakly annotated images to achieve the image patches as the training set. The detection problem is formulated into a deep learning framework using these patches efficiently. Then, the feature extraction is made through the training of the deep convolutional neural networks. A challenging clinical use case including hundreds of patients’ lung cancer histopathological images is used in our experiment. Our method has achieved promising performance on the lung cancer cell detection in terms of accuracy and efficiency.
Z. Huang—This work was partially supported by U.S. NSF IIS-1423056, CMMI-1434401, CNS-1405985.
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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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Pan, H., Xu, Z., Huang, J. (2015). An Effective Approach for Robust Lung Cancer Cell Detection. 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_11
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DOI: https://doi.org/10.1007/978-3-319-28194-0_11
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