Efficient Lung Cancer Cell Detection with Deep Convolution Neural Network

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9467)


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.


Cell Area Image Patch Testing Stage Training Strategy Training Stage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA

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