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Towards Interactive Breast Tumor Classification Using Transfer Learning

  • Nick Weiss
  • Henning Kost
  • André Homeyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)

Abstract

The diagnosis of breast cancer relies on the accurate classification of morphological subtypes in histological sections. Recent advances in image analysis using convolutional neural networks have yielded promising automated methods for this classification task. These networks are usually trained from scratch and depend on hours-long training with thousands of labeled examples to produce good results. Once trained these methods can not easily be adapted in cases of misclassification or to novel tasks. We aim to develop methods that can quickly be adapted in an interactive way. As a first step in this direction we present a classification method that enables fast training with a limited number of samples and achieves state-of-the-art results.

Keywords

Histology Breast cancer Automated image analysis Transfer learning Neural networks 

Notes

Acknowledgements

This work was conducted under the QuantMed project funded by the Fraunhofer Society, Munich, Germany.

References

  1. 1.
    Araújo, T., Aresta, G., Aguiar, P., Eloy, C., Polónia, A.: Bach challenge (2018). https://iciar2018-challenge.grand-challenge.org
  2. 2.
    Araújo, T., Aresta, G., Castro, E., et al.: Classification of breast cancer histology images using Convolutional Neural Networks. PLOS ONE 12(6), e0177544 (2017)CrossRefGoogle Scholar
  3. 3.
    Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras
  4. 4.
    Chollet, F.: Xception - deep learning with depthwise separable convolutions. In: CVPR, pp. 1800–1807 (2017)Google Scholar
  5. 5.
    Cruz-Roa, A., Basavanhally, A., González, F., et al.: Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: SPIE, pp. 904103–904115 (2014)Google Scholar
  6. 6.
    Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)Google Scholar
  7. 7.
    Ehteshami Bejnordi, B., Litjens, G., Timofeeva, N., et al.: Stain specific standardization of whole-slide histopathological images. IEEE Trans. Med. Imaging 35(2), 404–415 (2016)CrossRefGoogle Scholar
  8. 8.
    Elmore, J.G., Longton, G.M., Carney, P.A., et al.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313(11), 1122–1132 (2015)CrossRefGoogle Scholar
  9. 9.
    Ferlay, J., Soerjomataram, I., Dikshit, R., et al.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5), E359–86 (2015)CrossRefGoogle Scholar
  10. 10.
    Ghaznavi, F., Evans, A., Madabhushi, A., Feldman, M.: Digital imaging in pathology: whole-slide imaging and beyond. Ann. Rev. Pathol. 8, 331–359 (2013)CrossRefGoogle Scholar
  11. 11.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)Google Scholar
  12. 12.
    Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inf. 7(1), 29–18 (2016)CrossRefGoogle Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25, pp. 1097–1105 (2012)Google Scholar
  14. 14.
    Li, W., Manivannan, S., Akbar, S., et al.: Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks. In: ISBI, pp. 1405–1408 (2017)Google Scholar
  15. 15.
    Litjens, G., Kooi, T., Bejnordi, B.E., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 1–29 (2017)CrossRefGoogle Scholar
  16. 16.
    Macenko, M., Niethammer, M., Marron, J.S., et al.: A method for normalizing histology slides for quantitative analysis. In: ISBI, pp. 1107–1110 (2009)Google Scholar
  17. 17.
    Madabhushi, A., Lee, G.: Image analysis and machine learning in digital pathology: challenges and opportunities, pp. 1–6 (2016)Google Scholar
  18. 18.
    Makki, J.: Diversity of breast carcinoma: histological subtypes and clinical relevance. Clin. Med. Insights Pathol. 8, 23–31 (2015). CPath.S31563–9Google Scholar
  19. 19.
    Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn - machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Senkus, E., Kyriakides, S., Penault-Llorca, F., et al.: Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 24(Suppl. 6), vi7–vi23 (2013)CrossRefGoogle Scholar
  21. 21.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Computing Research Repository (2014)Google Scholar
  22. 22.
    Spanhol, F.A., Oliveira, L.S., Cavalin, P.R., Petitjean, C., Heutte, L.: Deep features for breast cancer histopathological image classification. In: SMC, pp. 1868–1873 (2017)Google Scholar
  23. 23.
    Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016)CrossRefGoogle Scholar
  24. 24.
    Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using Convolutional Neural Networks. In: IJCNN (2016)Google Scholar
  25. 25.
    Veta, M., Pluim, J.P.W., Van Diest, P.J., Viergever, M.A.: Breast cancer histopathology image analysis: a review. IEEE Trans. Biomed. Eng. 61(5), 1400–1411 (2014)CrossRefGoogle Scholar
  26. 26.
    Xu, Y.: Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinform. 18(1), 1–17 (2017)CrossRefGoogle Scholar
  27. 27.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol. 2, pp. 3320–3328 (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Fraunhofer MEVISLübeckGermany
  2. 2.Fraunhofer MEVISBremenGermany

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