Coherence-regularized discriminative dictionary learning for histopathological image classification

  • Hongzhong TangEmail author
  • Xiao Li
  • Xiaogang Zhang
  • Dongbo Zhang
  • Lizhen Mao
  • Ting Liu
Original Paper


In this paper, a novel coherence-regularized discriminative dictionary learning (CRDDL) algorithm is proposed to deal with histopathological image classification. By incorporating two constructed special regularization terms into an objective function, i.e., the self-coherence within each intra-class dictionary and the mutual coherence between inter-class dictionaries, high-quality discriminative healthy and diseased dictionaries can both be explicitly learned. Furthermore, to balance the reconstruction and discrimination abilities of learned dictionaries, we minimize the reconstruction error of intra-class samples and maximize the reconstruction error of inter-class samples. Finally, reconstruction error vectors are employed to design the classifier of histopathological images. Experimental results demonstrate the improved performance of the proposed CRDDL algorithm in comparison with other previously reported discriminative dictionary learning algorithms.


Discriminative dictionary learning Self-coherence within intra-class dictionary Mutual coherence between inter-class dictionaries Histopathological image classification 



This article is supported by the National Natural Science Foundation in china (61573299, 61602397) and the Natural Science Foundation of Hunan Province in China (2017JJ3315, 2017JJ2251, 2016JJ3125).


  1. 1.
    Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: gland segmentation and structural features. Pattern Recognit. Lett. 33(7), 951–961 (2012)Google Scholar
  2. 2.
    Madabhushi, A., Lee, G.: Image analysis and machine learning in digital pathology: challenges and opportunities. Med. Image. Anal. 33, 170–175 (2016)Google Scholar
  3. 3.
    McCann, M.T., Ozolek, J.A., Castro, C.A., Parvin, B., Kovacevic, J.: Automated histology analysis: opportunities for signal processing. IEEE Signal Process. Mag. 32(1), 78–87 (2015)Google Scholar
  4. 4.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)Google Scholar
  5. 5.
    Srinivas, U., Mousavi, H., Jeon, C., Monga, V., Hattel, A., B. Jayarao, Shirc: a simultaneous sparsity model for histopathological image representation and classification. In: 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), IEEE, pp. 1118–1121 (2013)Google Scholar
  6. 6.
    Srinivas, U., Mousavi, H.S., Monga, V., Hattel, A., Jayarao, B.: Simultaneous sparsity model for histopathological image representation and classification. IEEE Trans. Med. Imaging 33(5), 1163–1179 (2014)Google Scholar
  7. 7.
    Nayak, N., Chang, H., Borowsky, A., Spellman, P., Parvin, B.: Classification of tumor histopathology via sparse feature learning. In: 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI), IEEE, pp. 410–413 (2013)Google Scholar
  8. 8.
    Zhou, Y., Chang, H., Barner, K., Spellman, P., Parvin, B.: Classification of histology sections via multispectral convolutional sparse coding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3081–3088 (2014)Google Scholar
  9. 9.
    Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., Madabhushi, A.: Stacked sparse autoencoder (ssae) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35(1), 119–130 (2016). Google Scholar
  10. 10.
    Chang, H., Zhou, Y., Borowsky, A., Barner, K., Spellman, P., Parvin, B.: Stacked predictive sparse decomposition for classification of histology sections. Int. J. Comput. Vis. 113(1), 3–18 (2015)MathSciNetGoogle Scholar
  11. 11.
    Shi, Y., Gao, Y., Yang, Y., Zhang, Y., Wang, D.: Multimodal sparse representation-based classification for lung needle biopsy images. IEEE Trans. Biomed. Eng. 60(10), 2675–2685 (2013)Google Scholar
  12. 12.
    Zhang, Q., Li, B.: Discriminative k-svd for dictionary learning in face recognition. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 2691–2698 (2010)Google Scholar
  13. 13.
    Jiang, Z., Lin, Z., Davis, L.S.: Label consistent k-svd: learning a discriminative dictionary for recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2651–2664 (2013)Google Scholar
  14. 14.
    Yang, M., Zhang, L., Feng, X., Zhang, D.: Fisher discrimination dictionary learning for sparse representation. In: 2011 IEEE International Conference on Computer Vision (ICCV), IEEE, pp. 543–550 (2011)Google Scholar
  15. 15.
    Yang, M., Dai, D., Shen, L., Van Gool, L.: Latent dictionary learning for sparse representation based classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4138–4145 (2014)Google Scholar
  16. 16.
    Yang, M., Chang, H., Luo, W., Yang, J.: Fisher discrimination dictionary pair learning for image classification. Neurocomputing 269, 13–20 (2017)Google Scholar
  17. 17.
    Xu, M., Dong, H., Chen, C., Li, L.: Unsupervised dictionary learning with fisher discriminant for clustering. Neurocomputing 194, 65–73 (2016)Google Scholar
  18. 18.
    Vu, T.H., Mousavi, H.S., Monga, V., Rao, U.A, Rao G.: Dfdl: Discriminative feature-oriented dictionary learning for histopathological image classification. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), IEEE, pp. 990–994 (2015)Google Scholar
  19. 19.
    Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K., Li, S.: Breast cancer multiclassification from histopathological images with structured deep learning model. Sci. Rep. 7(1), 1–10 (2017)Google Scholar
  20. 20.
    Xu, Y., Jia, Z., Wang, L., Ai, Y., Zhang, F., Lai, M., Chang, E.I.-C.: Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinform. 18(1), 281 (2017)Google Scholar
  21. 21.
    Shi, X., Sapkotab, M., Xing, F., Liu, F., Cui, L., Yang, L.: Pairwise based deep ranking hashing for histopathology image classification and retrieval. Pattern Recognit. 81, 14–22 (2018)Google Scholar
  22. 22.
    Hong-Zhong, T., Xiao, L., Xiao-Gang, Z., Dong-Bo, Z., Xiang, W.: Discriminative feature-oriented dictionary learning method with fisher criterion for histopathological image classification. Acta Automatica Sinica 44(10), 1842–1853 (2018)Google Scholar
  23. 23.
    Donoho, D.L., Huo, X.: Uncertainty principles and ideal atomic decomposition. IEEE Trans. Inf. Theory 47(7), 2845–2862 (2001)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Donoho, D.L., Elad, M.: On the stability of the basis pursuit in the presence of noise. Signal Process. 86(3), 511–532 (2006)zbMATHGoogle Scholar
  25. 25.
    Ramirez, I., Sprechmann, P., Sapiro, G.: Classification and clustering via dictionary learning with structured incoherence and shared features. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 3501–3508 (2010)Google Scholar
  26. 26.
    Bao, C., Quan, Y., Ji, H.: A convergent incoherent dictionary learning algorithm for sparse coding. In: European Conference on Computer Vision, pp. 302–316. Springer, New York (2014)Google Scholar
  27. 27.
    Hong-Zhong, T., Xiao-Gang, Z., Hua, C., Wei, C., Mei-Ling, T.: Incoherent dictionary learning method with border condition constrained for sparse representation. Acta Automatica Sinica 41(2), 312–319 (2015)Google Scholar
  28. 28.
    Tang, H., Zhang, X., Chen, H., Zhu, L., Wang, X., Li, X.: Incoherent dictionary learning method based on unit norm tight frame and manifold optimization for sparse representation. Math. Prob. Eng. 2016, 1–10 (2016). MathSciNetzbMATHGoogle Scholar
  29. 29.
    Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Information EngineeringXiangtan UniversityHunanChina
  2. 2.College of Electrical and Information EngineeringHunan UniversityHunanChina
  3. 3.Key Laboratory of Intelligent Computing and Information Processing of Ministry of EducationXiangtan UniversityHunanChina

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