Skip to main content
SpringerLink
Log in

Heterogeneous ensemble with information theoretic diversity measure for human epithelial cell image classification

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

In this work, we propose a heterogeneous committee (ensemble) of diverse members (classification approaches) to solve the problem of human epithelial (HEp-2) cell image classification using indirect Immunofluorescence (IIF) imaging. We hypothesize that an ensemble involving different feature representations can enable higher performance if individual members in the ensemble are sufficiently varied. These members are of two types: (1) CNN-based members, (2) traditional members. For the CNN members, we have employed the well-established ResNet, DenseNet, and Inception models, which have distinctive salient aspects. For the traditional members, we incorporate class-specific features which are characterized depending on visual morphological attributes, and some standard texture features. To select the members which are discriminating and not redundant, we use an information theoretic measure which considers the trade-off between individual accuracies and diversity among the members. For all selected members, a compelling fusion required to combine their outputs to reach a final decision. Thus, we also investigate various fusion methods that combine the opinion of the committee at different levels: maximum voting, product, decision template, Bayes, Dempster-Shafer, etc. The proposed method is evaluated using ICPR-2014 data which consists of more images than some previous datasets ICPR-2012 and demonstrate state-of-the-art performance. To check the effectiveness of the proposed methodology for other related datasets, we test our methodology with newly compiled large-scale HEp-2 dataset with 63K cell images and demonstrate comparable performance even with less number of training samples. The proposed method produces 99.80% and 86.03% accuracy respectively when tested on ICPR-2014 and a new large-scale data containing 63K samples.

Overview of the proposed methodology

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Wiik AS, Høier-madsen M, Forslid J, Charles P, Meyrowitsch J (2010) Antinuclear antibodies: a contemporary nomenclature using HEp-2 cells. J Autoimmun 35(3):276–290

    Article  CAS  Google Scholar 

  2. Meroni PL, Schur PH (2010) ANA screening: an old test with new recommendations. Ann Rheum Diseases 69(8):1420–1422

    Article  CAS  Google Scholar 

  3. Kumar Y, Bhatia A, Minz RW (2009) Antinuclear antibodies and their detection methods in diagnosis of connective tissue diseases: a journey revisited. Diag Path 4(1):1–10

    Article  CAS  Google Scholar 

  4. Hiemann R, Büttner T, Krieger T, Roggenbuck D, Sack U, Conrad K (2009) Challenges of automated screening and differentiation of non-organ specific autoantibodies on HEp-2 cells. Autoimmun Rev 9(1):17–22

    Article  CAS  Google Scholar 

  5. Sack U, Knoechner S, Warschkau H, Pigla U, Emmrich F, Kamprad M (2003) Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics. Autoimmun Rev 2 (5):298–304

    Article  CAS  Google Scholar 

  6. Hiemann R, Hilger N, Sack U, Weigert M (2006) Objective quality evaluation of fluorescence images to optimize automatic image acquisition. Cytometry Part A 69(3):182–184

    Article  Google Scholar 

  7. Pham B, Albarede S, Guyard A, Burg E, Maisonneuve P (2005) Impact of external quality assessment on antinuclear antibody detection performance. Lupus 14(2):113–119

    Article  CAS  Google Scholar 

  8. Bell DA, Guan JwW, Bi Y (2005) On combining classifier mass functions for text categorization. IEEE Trans Knowl Data Eng 17(10):1307–1319

    Article  Google Scholar 

  9. Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241–259

    Article  Google Scholar 

  10. Pajares G, Guijarro M, Ribeiro A (2010) A hopfield neural network for combining classifiers applied to textured images. Neural Netw 23(1):144–153

    Article  Google Scholar 

  11. Su Y, Shan S, Chen X, Gao W (2009) Hierarchical ensemble of global and local classifiers for face recognition. IEEE Trans Image Process 18(8):1885–1896

    Article  Google Scholar 

  12. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  13. Ponti MP Jr (2011) Combining classifiers: from the creation of ensembles to the decision fusion. In: 2011 24th SIBGRAPI conference on graphics, patterns and images tutorials (SIBGRAPI-T). IEEE, pp 1–10

  14. Wolpert DH (2002) The supervised learning no-free-lunch theorems. In: Soft computing and industry. Springer, pp 25–42

  15. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  16. Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 806–813

  17. Shen H, Manivannan S, Annunziata R, Wang R, Zhang J (2016) Combination of CNN and hand-crafted feature for Ischemic stroke lesion segmentation. Ischemic Stroke Lesion Segment: 1

  18. Li W, Manivannan S, Akbar S, Zhang J, Trucco E, McKenna SJ (2016) Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks. In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, pp 1405–1408

  19. Meynet J, Thiran JP (2010) Information theoretic combination of pattern classifiers. Patt Recogn 43(10):3412–3421

    Article  Google Scholar 

  20. Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51(2):181–207

    Article  Google Scholar 

  21. Brown G, Wyatt J, Harris R, Yao X (2005) Diversity creation methods: a survey and categorisation. Inf Fus 6(1):5–20

    Article  Google Scholar 

  22. Qi X, Zhao G, Chen J, Pietikäinen M (2016) Exploring illumination robust descriptors for human epithelial type 2 cell classification. Patt Recogn 60:420–429

    Article  Google Scholar 

  23. Percannella G, Foggia P, Soda P (2012) Contest on HEp-2 cells classification. In: IEEE international conference on pattern recognition (ICPR)

  24. Wiliem A, Wong Y, Sanderson C, Hobson P, Chen S, Lovell BC (2013) Classification of human epithelial type 2 cell indirect immunofluoresence images via codebook based descriptors. In: 2013 IEEE workshop on applications of computer vision (WACV). IEEE, pp 95–102

  25. Hobson P, Lovell BC, Percannella G, Vento M, Wiliem A (2015) Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset. Artif Intell Med 65(3):239–250

    Article  Google Scholar 

  26. Lovell B, Percannella G, Vento M, Wiliem A (2014) 1st workshop on pattern recognition techniques for indirect immunofluorescence images. In: IEEE international conference on pattern recognition (ICPR)

  27. Foggia P, Percannella G, Soda P, Vento M (2010) Early experiences in mitotic cells recognition on HEp-2 slides. In: 2010 IEEE 23rd international symposium on computer-based medical systems (CBMS). IEEE, pp 38–43

  28. Iannello G, Percannella G, Soda P, Vento M (2014) Mitotic cells recognition in HEp-2 images. Patt Recogn Lett 45:136–144

    Article  Google Scholar 

  29. Hobson P, Lovell BC, Percannella G, Saggese A, Vento M, Wiliem A (2016) HEP-2 staining pattern recognition at cell and specimen levels: datasets, algorithms and results. Patt Recogn Lett 82:12–22

    Article  Google Scholar 

  30. Hobson P, Lovell BC, Percannella G, Saggese A, Vento M, Wiliem A (2016) Computer aided diagnosis for anti-nuclear antibodies HEp-2 images: progress and challenges. Patt Recogn Lett 82:3–11

    Article  Google Scholar 

  31. Cascio D, Taormina V, Cipolla M, Bruno S, Fauci F, Raso G (2016) A multi-process system for HEp-2 cells classification based on SVM. Patt Recogn Lett 82:56–63

    Article  Google Scholar 

  32. Ensafi S, Lu S, Kassim AA, Tan CL (2016) Accurate HEp-2 cell classification based on sparse coding of superpixels. Patt Recogn Lett 82:64–71

    Article  Google Scholar 

  33. Han XH, Chen YW, Xu G (2016) Integration of spatial and orientation contexts in local ternary patterns for HEp-2 cell classification. Patt Recogn Lett 82:23–27

    Article  Google Scholar 

  34. Gragnaniello D, Sansone C, Verdoliva L (2016) Cell image classification by a scale and rotation invariant dense local descriptor. Patt Recogn Lett 82:72–78

    Article  Google Scholar 

  35. Nanni L, Lumini A, dos Santos FLC, Paci M, Hyttinen J (2016) Ensembles of dense and dense sampling descriptors for the HEp-2 cells classification problem. Patt Recogn Lett 82:28–35

    Article  Google Scholar 

  36. Chandran V, Banks J, Boles W, Chen B, Tomeo-Reyes I (2013) Cell image classification using histograms, higher order statistics and adaboost. In: IEEE International Conference on Image Processing (ICIP) - Cells Classification by Fluorescent Image Analysis Competition: 11

  37. Nanni L, Paci M, dos Santos FLC, Hyttinen J (2014) Morphological and texture features for HEp-2 cells classification. In: 1st workshop on pattern recognition techniques for indirect immunofluorescence images (I3A). IEEE, pp 45–48

  38. Theodorakopoulos I, Kastaniotis D, Economou G, Fotopoulos S (2014) HEp-2 cells classification using morphological features and a bundle of local gradient descriptors. In: 1st workshop on pattern recognition techniques for indirect immunofluorescence images (I3A). IEEE, pp 33–36

  39. Theodorakopoulos I, Kastaniotis D, Economou G, Fotopoulos S (2012) HEp-2 cells classification via fusion of morphological and textural features. In: 12th international conference on bioinformatics & bioengineering (BIBE). IEEE, pp 689–694

  40. Donato C, Vincenzo T, Marco C, Francesco F, Maria VS, Giuseppe R (2014) HEp-2 cell classification with heterogeneous classes-processes based on k-nearest neighbours. In: 1st workshop on pattern recognition techniques for indirect immunofluorescence images (I3A). IEEE, pp 10–15

  41. Ensafi S, Lu S, Kassim AA, Tan CL (2014) A bag of words based approach for classification of HEp-2 cell images. In: 1st workshop on pattern recognition techniques for indirect immunofluorescence images (I3A). IEEE, pp 29–32

  42. Schapire RE (1990) The strength of weak learnability. Mach Learn 5(2):197–227

    Google Scholar 

  43. Freund Y, Schapire RE et al (1996) Experiments with a new boosting algorithm. In: icml, vol 96, pp 148–156

  44. Manivannan S, Li W, Akbar S, Wang R, Zhang J, McKenna SJ (2014) HEp-2 cell classification using multi-resolution local patterns and ensemble SVMs. In: 1st workshop on pattern recognition techniques for indirect immunofluorescence images (I3A). IEEE, pp 37–40

  45. Manivannan S, Li W, Akbar S, Wang R, Zhang J, McKenna SJ (2016) An automated pattern recognition system for classifying indirect immunofluorescence images of HEp-2 cells and specimens. Patt Recogn 51:12–26

    Article  Google Scholar 

  46. Gao Z, Zhang J, Zhou L, Wang L (2014) Hep-2 cell image classification with convolutional neural networks. In: 2014 1st workshop on pattern recognition techniques for indirect immunofluorescence images (I3A). IEEE, pp 24–28

  47. Gao Z, Wang L, Zhou L, Zhang J (2017) HEP-2 cell image classification with deep convolutional neural networks. IEEE J Biomed Health Inf 21(2):416–428

    Article  Google Scholar 

  48. Rodrigues LF, Naldi MC, Mari JF (2017) Exploiting Convolutional Neural Networks and preprocessing techniques for HEp-2 cell classification in immunofluorescence images. In: 2017 30th SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, pp 170–177

  49. Jia X, Shen L, Zhou X, Yu S (2016) Deep convolutional neural network based HEp-2 cell classification. In: IEEE, pp 77–80

  50. Li Y, Shen L (2018) HEP-net: a smaller and better deep-learning network for HEp-2 cell classification. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 7(3):1–7

    Google Scholar 

  51. Phan HTH, Kumar A, Kim J, Feng D (2016) Transfer learning of a convolutional neural network for HEp-2 cell image classification. In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, pp 1208–1211

  52. Bayramoglu N, Kannala J, Heikkilä J (2015) Human epithelial type 2 cell classification with convolutional neural networks. In: 2015 IEEE 15th international conference on bioinformatics and bioengineering (BIBE). IEEE, pp 1–6

  53. Liu J, Xu B, Shen L, Garibaldi J, Qiu G (2017) HEp-2 cell classification based on a deep autoencoding-classification convolutional neural network. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017). IEEE, pp 1019–1023

  54. Wen G, Hou Z, Li H, Li D, Jiang L, Xun E (2017) Ensemble of deep neural networks with probability-based fusion for facial expression recognition. Cogn Comput 9(5):597–610

    Article  Google Scholar 

  55. Kim BK, Roh J, Dong SY, Lee SY (2016) Hierarchical committee of deep convolutional neural networks for robust facial expression recognition. J Multimodal User Interf 10(2):173–189

    Article  Google Scholar 

  56. Gupta V, Gupta K, Bhavsar A, Sao AK (2016) Hierarchical classification of HEp-2 cell images using class-specific features. In: 2016 6th European workshop on visual information processing (EUVIP), vol 7, no. 3. IEEE, pp 1–6

  57. Gupta K, Gupta V, Sao AK, Bhavsar A, Dileep AD (2014) Class-specific hierarchical classification of HEp-2 cell images: the case of two classes. In: 2014 1st workshop on pattern recognition techniques for indirect immunofluorescence images. IEEE , pp 6–9

  58. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  59. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: CVPR, vol 1, p 3

  60. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  61. Strandmark P, Ulén J, Kahl F (2012) HEp-2 staining pattern classification. In: 21st international conference on pattern recognition (ICPR). IEEE, pp 33–36

  62. Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Sys Technol (TIST) 2(3):27

    Google Scholar 

  63. Chen T, Guestrin C (2016) XGBoost: reliable large-scale tree boosting system. arXiv. 2016a ISSN. pp 0146–4833

  64. Kuncheva LI, Whitaker CJ (2002) Using diversity with three variants of boosting: aggressive, conservative, and inverse. In: International workshop on multiple classifier systems. Springer, pp 81–90

  65. Xu L, Krzyzak A, Suen CY (1992) Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans Sys Man Cybern 22(3):418–435

    Article  Google Scholar 

  66. Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, New York

    Book  Google Scholar 

  67. Singh D, Singh B (2019) Investigating the impact of data normalization on classification performance. Appl Soft Comput 97 Part B:105524

    Google Scholar 

  68. Erhan D, Manzagol PA, Bengio Y, Bengio S, Vincent P (2009) The difficulty of training deep architectures and the effect of unsupervised pre-training. In: Artificial Intelligence and Statistics, pp 153–160

  69. Margeta J, Criminisi A, Cabrera Lozoya R, Lee DC, Ayache N (2017) Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition. Comput Methods Biomech Biomed Eng Imag Visual 5(5):339–349

    Article  Google Scholar 

  70. Kumar A, Sridar P, Quinton A, Kumar RK, Feng D, Nanan R et al (2016) Plane identification in fetal ultrasound images using saliency maps and convolutional neural networks. In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, pp 791–794

  71. Bayramoglu N, Heikkilä J (2016) Transfer learning for cell nuclei classification in histopathology images. In: European conference on computer vision. Springer, pp 532–539

  72. Penatti OA, Nogueira K, dos Santos JA (2015) Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 44–51

  73. Azizpour H, Sharif Razavian A, Sullivan J, Maki A, Carlsson S (2015) From generic to specific deep representations for visual recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 36–45

Download references

Author information

Authors and Affiliations

  1. School of Computer and Electrical Engineering, Indian Institute of Technology, Himachal Pradesh, Mandi, 175005, India

    Vibha Gupta & Arnav Bhavsar

Authors
  1. Vibha Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arnav Bhavsar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vibha Gupta.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Author contributions

The first author was primarily responsible for the work reported in this paper. The second author played an advisory role involving various discussions. The second author is the Ph.D. advisor of the first author.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, V., Bhavsar, A. Heterogeneous ensemble with information theoretic diversity measure for human epithelial cell image classification. Med Biol Eng Comput 59, 1035–1054 (2021). https://doi.org/10.1007/s11517-021-02336-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-021-02336-8

Keywords

Search

Navigation