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A Fast Automatic Juxta-pleural Lung Nodule Detection Framework Using Convolutional Neural Networks and Vote Algorithm

  • Jiaxing Tan
  • Yumei Huo
  • Zhengrong Liang
  • Lihong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11075)

Abstract

Lung Nodule Detection from CT scans is a crucial task for the early detection of lung cancer with high difficulty performing an automatic detection. In this paper, we propose a fast automatic voting based framework using Convolutional Neural Network to detect juxta-pleural nodules, which are pulmonary (lung) nodules attached to the chest wall and hard to detect even by human experts. The detection result for each region in the CT scan is voted by the detection results of the extracted candidates from the region, which we formulate as a generative model. We perform two sets of experiments: one is to validate our framework, and the other is to compare different convolution neural network settings under our framework. The result shows our framework is competent to detect juxta-pleural lung nodules especially when only a weak classifier trained on noisy data is available. Meanwhile, we overcome the problem of determining the proper input size for nodules with high variance in diameters.

Keywords

Lung cancer Juxta-pleural nodule detection Deep learning Weakly labeled 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jiaxing Tan
    • 1
  • Yumei Huo
    • 1
  • Zhengrong Liang
    • 2
  • Lihong Li
    • 1
  1. 1.City University of New YorkNew York CityUSA
  2. 2.Stony Brook UniversityStony BrookUSA

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