Self-taught Learning with Residual Sparse Autoencoders for HEp-2 Cell Staining Pattern Recognition

  • Xian-Hua HanEmail author
  • JiandDe Sun
  • Lanfen Lin
  • Yen-Wei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)


Self-taught learning aims at obtaining compact and latent representations from data them-selves without previously manual labeling, which would be time-consuming and laborious. This study proposes a novel self-taught learning for more accurately reconstructing the raw data based on the sparse autoencoder. It is well known that autoencoder is able to learn latent features via setting the target values to be equal to the input data, and can be stacked for pursuing high-level feature learning. Motivated by the natural sparsity of data representation, sparsity has been imposed on the hidden layer responses of autoencoder for more effective feature learning. Although the conventional autoencoder-based feature learning aims at obtaining the latent representation via minimizing the approximation error of the input data, it is unavoidable to produce reconstruction residual error of the input data and thus some tiny structures are unable to be represented, which may be essential information for fine-grained image task such as medical image analysis. Even with the multiple-layer stacking for high-level feature pursuing in autoencoder-based learning strategy, the lost tiny structure in the former layers can not be recovered evermore. Therefore, this study proposes a residual sparse autoencoder for learning the latent feature representation of more tiny structures in the raw input data. With the unavoidably generated reconstruction residual error, we exploit another sparse autoencoder to pursuing the latent feature of the residual tiny structures and this self-taught learning process can continue until the representation residual error is enough small. We evaluate the proposed residual sparse autoencoding for self-taught learning the latent representations of HEp-2 cell image, and prove that promising performance for staining pattern recognition can be achieved compared with the conventional sparse autoencoder and the-state-of-the-art methods.


Self-taught learning Unsupervised feature learning Latent representation Residual sparse autoencoder HEp2 cell Staining patterns 


  1. 1.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision (ICCV1999), vol. 2, pp. 1150–1157 (1999)Google Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  3. 3.
    Sun, L., Shao, W., Zhang, D.Q.: High-order boltzmann machine-based unsupervised feature learning for multi-atlas segmentation. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)Google Scholar
  4. 4.
    Zhao, Z.J., Xu, T.D., Dai, C.Y.: Classifying images using restricted Boltzmann machines and convolutional neural networks. In: Proceedings of SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202U (21 July 2017)Google Scholar
  5. 5.
    Shin, H.-C., Orton, M.R., Collins, D.J., Doran, S.J., Leach, M.O.: Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans. Pattern Anal. Mach. Intel. 35(8), 1930–1943 (2013)CrossRefGoogle Scholar
  6. 6.
    Xu, J., et al.: Stacked Sparse Autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35, 119–130 (2015)CrossRefGoogle Scholar
  7. 7.
    Foggia, P., Percannella, G., Soda, P., Vento, M.: Benchmarking hep-2 cells classification methods. IEEE Trans. Med. Imaging 32(10), 1878–1889 (2013)CrossRefGoogle Scholar
  8. 8.
    Agrawal, P., Vatsa, M., Singh, R.: HEp-2 cell image classification: a comparative analysis. In: Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K., Wang, F. (eds.) MLMI 2013. LNCS, vol. 8184, pp. 195–202. Springer, Cham (2013). Scholar
  9. 9.
    Manivannan, S., Li, W., Akbar, S., Wang, R., Zhang, J., McKenna, S.J.: An automated pattern recognition system for classifying indirect immunofluorescence images of hep-2 cells and specimens. Pattern Recognit. 15, 12–26 (2016)CrossRefGoogle Scholar
  10. 10.
    Han, X.-H., Chen, Y.-W.: HEp-2 staining pattern recognition using stacked fisher network for encoding weber local descriptor. Pattern Recognit. 63, 542–550 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xian-Hua Han
    • 1
    Email author
  • JiandDe Sun
    • 2
  • Lanfen Lin
    • 3
  • Yen-Wei Chen
    • 4
  1. 1.Graduate School of Science and Technology for Innovation, Yamaguchi UniversityYamaguchiJapan
  2. 2.ShanDong Normal UniversityJianNanChina
  3. 3.Zhejiang UniversityHangzhouChina
  4. 4.Ritsumeikan UniversityKusatsuJapan

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