Advertisement

Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural Networks

  • Jan-Jurre Mordang
  • Tim Janssen
  • Alessandro Bria
  • Thijs Kooi
  • Albert Gubern-Mérida
  • Nico Karssemeijer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9699)

Abstract

Convolutional neural networks (CNNs) have shown to be powerful for classification of image data and are increasingly used in medical image analysis. Therefore, CNNs might be very suitable to detect microcalcifications in mammograms. In this study, we have configured a deep learning approach to fulfill this task. To overcome the large class imbalance between pixels belonging to microcalcifications and other breast tissue, we applied a hard negative mining strategy where two CNNs are used. The deep learning approach was compared to a current state-of-the-art method for the detection of microcalcifications: the cascade classifier. Both methods were trained on a large training set including 11,711 positive and 27 million negative samples. For testing, an independent test set was configured containing 5,298 positive and 18 million negative samples. The mammograms included in this study were acquired on mammography systems from three manufactures: Hologic, GE, and Siemens. Receiver operating characteristics analysis was carried out. Over the whole specificity range, the CNN approach yielded a higher sensitivity compared to the cascade classifier. Significantly higher mean sensitivities were obtained with the CNN on the mammograms of each individual manufacturer compared to the cascade classifier in the specificity range of 0 to 0.1. To our knowledge, this was the first study to use a deep learning strategy for the detection of microcalcifications in mammograms.

Keywords

Mammography Calcifications Deep learning Convolutional neural networks Computer aided detection 

References

  1. 1.
    Bornefalk, H., Hermansson, A.B.: On the comparison of FROC curves in mammography CAD systems. Med. Phys. 32, 412–417 (2005)CrossRefGoogle Scholar
  2. 2.
    Bria, A., Karssemeijer, N., Tortorella, F.: Learning from unbalanced data: a cascade-based approach for detecting clustered microcalcifications. Med. Image Anal. 18, 241–252 (2013)CrossRefGoogle Scholar
  3. 3.
    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, Part II. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Cruz-Roa, A.A., Arevalo Ovalle, J.E., Madabhushi, A., González Osorio, F.A.: A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 403–410. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap, vol. 57. CRC Press, Boca Raton (1994)zbMATHGoogle Scholar
  6. 6.
    Guo, Y., Wu, G., Commander, L.A., Szary, S., Jewells, V., Lin, W., Shen, D.: Segmenting hippocampus from infant brains by sparse patch matching with deep-learned features. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 308–315. Springer, Heidelberg (2014)Google Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015). arXiv:150201852v1
  8. 8.
    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
  9. 9.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  10. 10.
    Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: Proceedings of 2002 International Conference on Image Processing, vol. 1, pp. I-900–I-903 (2002)Google Scholar
  11. 11.
    Samuelson, F., Petrick, N.: Comparing image detection algorithms using resampling. In: IEEE International Symposium on Biomedical Imaging, pp. 1312–1315 (2006)Google Scholar
  12. 12.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRefGoogle Scholar
  13. 13.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:14091556
  14. 14.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Torralba, A., Murphy, K., Freeman, W.: Sharing visual features for multiclass and multiview object detection. IEEE Trans. Pattern Anal. Mach. Intell. 29, 854–869 (2007)CrossRefGoogle Scholar
  16. 16.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I-511–I-518 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jan-Jurre Mordang
    • 1
  • Tim Janssen
    • 1
  • Alessandro Bria
    • 2
  • Thijs Kooi
    • 1
  • Albert Gubern-Mérida
    • 1
  • Nico Karssemeijer
    • 1
  1. 1.Department of Radiology and Nuclear MedicineRadboud University Nijmegen Medical CenterNijmegenThe Netherlands
  2. 2.Department of Electrical and Information EngineeringUniversity of Cassino and Southern LazioCassinoItaly

Personalised recommendations