Breast Cancer Histological Image Classification Using Fine-Tuned Deep Network Fusion

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)


Breast cancer is the most common cancer type in women worldwide. Histological evaluation of the breast biopsies is a challenging task even for experienced pathologists. In this paper, we propose a fully automatic method to classify breast cancer histological images to four classes, namely normal, benign, in situ carcinoma and invasive carcinoma. The proposed method takes normalized hematoxylin and eosin stained images as input and gives the final prediction by fusing the output of two residual neural networks (ResNet) of different depth. These ResNets were first pre-trained on ImageNet images, and then fine-tuned on breast histological images. We found that our approach outperformed a previous published method by a large margin when applied on the BioImaging 2015 challenge dataset yielding an accuracy of 97.22%. Moreover, the same approach provided an excellent classification performance with an accuracy of 88.50% when applied on the ICIAR 2018 grand challenge dataset using 5-fold cross validation.


Breast cancer Histological images Classification Deep learning 



This project is supported by Horizon 2020 Framework of the European Union in the CaSR Biomedicine project, No. 675228.


  1. 1.
    Torre, L.A., Bray, F., Siegel, R.L., Ferlay, J., Lortet Tieulent, J., Jemal, A.: Global cancer statistics, 2012. CA Cancer J. Clin. 65(2), 87–108 (2015)CrossRefGoogle Scholar
  2. 2.
    Saadatmand, S., Bretveld, R., Siesling, S., Tilanus-Linthorst, M.M.A.: Influence of tumour stage at breast cancer detection on survival in modern times: population based study in 173 797 patients. BMJ 351, h4901 (2015)CrossRefGoogle Scholar
  3. 3.
    Myers, E.R., Moorman, P., Gierisch, J.M., Havrilesky, L.J., Grimm, L.J., Ghate, S., Davidson, B., Mongtomery, R.C., Crowley, M.J., McCrory, D.C.: Benefits and harms of breast cancer screening: a systematic review. JAMA 314(15), 1615–1634 (2015)CrossRefGoogle Scholar
  4. 4.
    Guray, M., Sahin, A.A.: Benign breast diseases: classification, diagnosis, and management. Oncol. 11(5), 435–449 (2006)Google Scholar
  5. 5.
    Malhotra, G.K., Zhao, X., Band, H., Band, V.: Histological, molecular and functional subtypes of breast cancers. Cancer Biol. Ther. 10(10), 955–960 (2010)CrossRefGoogle Scholar
  6. 6.
    Makki, J.: Diversity of breast carcinoma: histological subtypes and clinical relevance. Clin. Med. Insights Pathol. 8, 23 (2015)Google Scholar
  7. 7.
    Elmore, J.G., Longton, G.M., Carney, P.A., Geller, B.M., Onega, T., Tosteson, A.N.A., Nelson, H.D., Pepe, M.S., Allison, K.H., Schnitt, S.J.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313(11), 1122–1132 (2015)CrossRefGoogle Scholar
  8. 8.
    Robertson, S., Azizpour, H., Smith, K., Hartman, J.: Digital image analysis in breast pathology-from image processing techniques to artificial intelligence. Translational Research 194, 19–35 (2018)CrossRefGoogle Scholar
  9. 9.
    Kowal, M., Filipczuk, P., Obuchowicz, A., Korbicz, J., Monczak, R.: Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput. Biol. Med. 43(10), 1563–1572 (2013)CrossRefGoogle Scholar
  10. 10.
    Filipczuk, P., Fevens, T., Krzyzak, A., Monczak, R.: Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. IEEE Trans. Med. Imaging 32(12), 2169–2178 (2013)CrossRefGoogle Scholar
  11. 11.
    Brook, A., El-Yaniv, R., Isler, E., Kimmel, R., Meir, R., Peleg, D.: Breast cancer diagnosis from biopsy images using generic features and SVMs. Technical report, Technion - Israel Institute of Technology (2006)Google Scholar
  12. 12.
    Belsare, A.D., Mushrif, M.M., Pangarkar, M.A., Meshram, N.: Classification of breast cancer histopathology images using texture feature analysis. In: TENCON 2015–2015 IEEE Region 10 Conference, pp. 1–5. IEEE (2015)Google Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)Google Scholar
  14. 14.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  15. 15.
    Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., Campilho, A.: Classification of breast cancer histology images using convolutional neural networks. PloS One 12(6), e0177544 (2017)CrossRefGoogle Scholar
  16. 16.
    Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560–2567. IEEE (2016)Google Scholar
  17. 17.
    Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). Scholar
  18. 18.
    Cruz-Roa, A., Basavanhally, A., González, F., Gilmore, H., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., Madabhushi, A.: Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: SPIE Medical Imaging. International Society for Optics and Photonics, vol. 9041, pp. 904103 (2014)Google Scholar
  19. 19.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar
  20. 20.
    Mahbod, A., Ecker, R., Ellinger, I.: Skin lesion classification using hybrid deep neural networks. arXiv preprint arXiv:1702.08434 (2017)
  21. 21.
    Macenko, M., Niethammer, M., Marron, J.S., Borland, D., Woosley, J.T., Guan, X., Schmitt, C., Thomas, N.E.: A method for normalizing histology slides for quantitative analysis. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 1107–1110. IEEE (2009)Google Scholar
  22. 22.
    Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall, Inc., Englewood Cliffs (1989)zbMATHGoogle Scholar
  23. 23.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Pathophysiology and Allergy ResearchMedical University of ViennaViennaAustria
  2. 2.Department of Research and DevelopmentTissueGnostics GmbHViennaAustria
  3. 3.Department of Biomedical Engineering and Health Systems, Division of Biomedical ImagingKTH Royal Institute of TechnologyStockholmSweden

Personalised recommendations