HEp-2 cell classification based on a Deep Autoencoding-Classification convolutional neural network

Jingxin Liu, Bolei Xu, Linlin Shen, Jon Garibaldi, Guoping Qiu

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

17 Citations (Scopus)


In this paper, we present a novel deep learning model termed Deep Autoencoding-Classification Network (DACN) for HEp-2 cell classification. The DACN consists of an autoencoder and a normal classification convolutional neural network (CNN), while the two architectures shares the same encoding pipeline. The DACN model is jointly optimized for the classification error and the image reconstruction error based on a multi-task learning procedure. We evaluate the proposed model using the publicly available ICPR2012 benchmark dataset. We show that this architecture is particularly effective when the training dataset is small which is often the case in medical imaging applications. We present experimental results to show that the proposed approach outperforms all known state of the art HEp-2 cell classification methods.

Original languageEnglish
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781509011711
Publication statusPublished - 15 Jun 2017
Externally publishedYes
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: 18 Apr 201721 Apr 2017

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference14th IEEE International Symposium on Biomedical Imaging, ISBI 2017


  • Autoencoder
  • Classification
  • Convolutional neural networks
  • HEp2 cells
  • Indirect immunofluorescence

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