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HEp-2 specimen classification via deep CNNs and pattern histogram

HEp-2 specimen classification via deep CNNs and pattern histogram

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Authors

  • Hongwei Li
  • Hao Huang (Lead / Corresponding author)
  • Wei-Shi Zheng
  • Xiaohua Xie,
  • Jianguo Zhang

Research units

Info

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR)
PublisherIEEE
Pages2145-2149
Number of pages5
ISBN (Electronic)9781509048472
ISBN (Print)9781509048472
StatePublished - 24 Apr 2017
Event23rd International Conference on Pattern Recognition - Cancún, Mexico

Conference

Conference23rd International Conference on Pattern Recognition
Abbreviated titleICPR 2016
CountryMexico
CityCancún
Period4/12/168/12/16
Internet address

Abstract

Automatic classification of Human Epithelial Type-2 (HEp-2) specimen patterns is an important yet challenging problem in medical image analysis. Most prior works have primarily focused on cells images classification problem which is one of the early essential steps in the system pipeline, while less attention has been paid to the classification of whole-specimen ones. In this work, a specimen pattern recognition system combining convolutional neural networks (CNNs) and pattern histogram was proposed. The pattern histograms were obtained based on the prediction of each single cell inside the specimens. Two strategies were designed to predicted the pattern of a whole specimen: 1) the most dominant cell pattern in pattern histogram was represented as the specimen pattern, 2) the pattern histograms were employed as bags of patterns and then were trained and predicted separately by a SVM classifier. Experimental results show that the proposed system is effective and achieves high classification accuracy on public benchmark datasets. We further evaluate the robustness of the proposed framework by testing trained CNNs on another different dataset, demonstrating that the system is robust to inter-lab data.

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