HEp-2 specimen classification with fully convolutional network

Yuexiang Li, Linlin Shen, Xiande Zhou, Shiqi Yu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)


Reliable automatic system for Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of systemic autoimmune diseases. In this paper, an automatic pattern recognition system using fully convolutional network (FCN) was proposed to address the HEp-2 specimen classification problem. The FCN in the proposed framework was adapted from VGG-16, which was trained with ICPR 2016 dataset to classify specimen images into seven catalogs: homogeneous, speckled, nucleolar, centromere, golgi, nuclear membrane, and mitotic spindle. The proposed system achieves a mean class accuracy of 90.89% for 5 fold-cross-validation tests using the I3A Contest Task 2 dataset, which is comparable to the winner of ICPR 2014, i.e. 89.93%. Furthermore, since the FCN was firstly developed for semantic segmentation, the proposed framework can simultaneously solve Task 4, Cell segmentation, newly suggested in I3A Contest 2016. The segmentation accuracy of the system is 87.38% on Task 4 dataset which is 17.4% higher than that of the traditional approach, Otsu, i.e. 69.98%.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509048472
Publication statusPublished - 1 Jan 2016
Externally publishedYes
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference23rd International Conference on Pattern Recognition, ICPR 2016


  • Cell patterns
  • Classification
  • Fully convolutional network
  • Segmentation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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