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About this Publication
Title
Efficient lung cancer cell detection with deep convolution neural network.
Digital Object Identifier
ISBN-13
9783319281940
ISBN-10
3319281941
Publication
Patch-MI 2015. 2016 Jan 8; Volume 9467: Pages pp. 79-86
Authors

Zheng Xu
Junzhou Huang

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.

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