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Subtype Cell Detection with an Accelerated Deep Convolution Neural Network

  • Sheng Wang
  • Jiawen Yao
  • Zheng Xu
  • Junzhou HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)

Abstract

Robust cell detection in histopathological images is a crucial step in the computer-assisted diagnosis methods. In addition, recent studies show that subtypes play an significant role in better characterization of tumor growth and outcome prediction. In this paper, we propose a novel subtype cell detection method with an accelerated deep convolution neural network. The proposed method not only detects cells but also gives subtype cell classification for the detected cells. Based on the subtype cell detection results, we extract subtype cell related features and use them in survival prediction. We demonstrate that our proposed method has excellent subtype cell detection performance and our proposed subtype cell features can achieve more accurate survival prediction.

Keywords

Cell Detection Zernike Moment Subtype Cell Deep Neural Network Survival Prediction 
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.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Sheng Wang
    • 1
  • Jiawen Yao
    • 1
  • Zheng Xu
    • 1
  • Junzhou Huang
    • 1
    Email author
  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA

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