Breast Density Classification with Convolutional Neural Networks

  • Pablo Fonseca
  • Benjamin Castañeda
  • Ricardo Valenzuela
  • Jacques Wainer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10125)


Breast Density Classification is a problem in Medical Imaging domain that aims to assign an American College of Radiology’s BIRADS category (I-IV) to a mammogram as an indication of tissue density. This is performed by radiologists in an qualitative way, and thus subject to variations from one physician to the other. In machine learning terms it is a 4-ordered-classes classification task with highly unbalance training data, as classes are not equally distributed among populations, even with variations among ethnicities. Deep Learning techniques in general became the state-of-the-art for many imaging classification tasks, however, dependent on the availability of large datasets. This is not often the case for Medical Imaging, and thus we explore Transfer Learning and Dataset Augmentationn. Results show a very high squared weighted kappa score of 0.81 (0.95 C.I. [0.77,0.85]) which is high in comparison to the 8 medical doctors that participated in the dataset labeling 0.82 (0.95 CI [0.77, 0.87]).


Mammographic Density Breast Density Deep Learn Convolutional Neural Network Kappa Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Boyle, P., Levin, B., et al.: World Cancer Report 2008. IARC Press, International Agency for Research on Cancer (2008)Google Scholar
  2. 2.
    Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37(5), 2486–2492 (1976)CrossRefGoogle Scholar
  3. 3.
    Boyd, N., Martin, L., Yaffe, M., Minkin, S.: Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Res. 13(6), 1 (2011)CrossRefGoogle Scholar
  4. 4.
    Ursin, G., Qureshi, S.A.: Mammographic density-a useful biomarker for breast cancer risk in epidemiologic studies. Nor. Epidemiol. 19(1), 59–68 (2009)Google Scholar
  5. 5.
    Casado, F.L., Manrique, S., Guerrero, J., Pinto, J., Ferrer, J., Castañeda, B.: Characterization of Breast Density in Women from Lima, Peru (2015)Google Scholar
  6. 6.
    Otsuka, M., Harkness, E.F., Chen, X., Moschidis, E., Bydder, M., Gadde, S., Lim, Y.Y., Maxwell, A.J., Evans, G.D., Howell, A., Stavrinos, P., Wilson, M., Astley, S.M.: Local Mammographic Density as a Predictor of Breast Cancer (2015)Google Scholar
  7. 7.
    D’Orsi, C.J.: Breast Imaging Reporting and Data System (BI-RADS). American College of Radiology (1998)Google Scholar
  8. 8.
    Oliver, A., Freixenet, J., Martí, R., Zwiggelaar, R.: A comparison of breast tissue classification techniques. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 872–879. Springer, Heidelberg (2006). doi: 10.1007/11866763_107 CrossRefGoogle Scholar
  9. 9.
    Oliver, A., Freixenet, J., Marti, R., Pont, J., Perez, E., Denton, E., Zwiggelaar, R.: A novel breast tissue density classification methodology. IEEE Trans. Inf. Technol. Biomed. 12(1), 55–65 (2008)CrossRefGoogle Scholar
  10. 10.
    Volpara Solutions (2015).
  11. 11.
    Hologic (2015).
  12. 12.
    Redondo, A., Comas, M., Maciá, F., Ferrer, F., Murta-Nascimento, C., Maristany, M.T., Molins, E., Sala, M., Castells, X.: Inter-and intraradiologist variability in the BI-RADS assessment and breast density categories for screening mammograms. Br. J. Radiol. 85(1019), 1465–1470 (2012). PMID: 22993385CrossRefGoogle Scholar
  13. 13.
    Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)CrossRefMATHGoogle Scholar
  14. 14.
    Fonseca, P., Mendoza, J., Wainer, J., Ferrer, J., Pinto, J., Guerrero, J., Castañeda, B.: Automatic Breast Density Classification using a Convolutional Neural Network Architecture Search Procedure (2015)Google Scholar
  15. 15.
    Pinto, N., Doukhan, D., DiCarlo, J.J., Cox, D.D.: A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS Comput. Biol. 5(11), e1000579 (2009)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  17. 17.
    Cox, D., Pinto, N.: Beyond simple features: a large-scale feature search approach to unconstrained face recognition. In: 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011), pp. 8–15 (2011)Google Scholar
  18. 18.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arXiv:1408.5093 (2014)
  19. 19.
    Angulo, A., Ferrer, J., Pinto, J., Lavarello, R., Guerrero, J., Castañeda, B.: Experimental Assessment of an Automatic Breast Density Classification Algorithm Based on Principal Component Analysis Applied to Histogram Data (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pablo Fonseca
    • 1
  • Benjamin Castañeda
    • 2
  • Ricardo Valenzuela
    • 3
  • Jacques Wainer
    • 1
  1. 1.RECOD Lab, Institute of ComputingUniversity of CampinasCampinasBrazil
  2. 2.Laboratorio de Imágenes MédicasPontificia Universidad Católica del PerúLimaPeru
  3. 3.Imaging LabEldorado Research InstituteCampinasBrazil

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