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A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography

  • Thijs Kooi
  • Albert Gubern-Merida
  • Jan-Jurre Mordang
  • Ritse Mann
  • Ruud Pijnappel
  • Klaas Schuur
  • Ard den Heeten
  • Nico Karssemeijer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9699)

Abstract

In this paper, we employ a deep Convolutional Neural Network (CNN) for the classification of regions of interest of malignant soft tissue lesions in mammography and show that it performs on par to experienced radiologists. The CNN was applied to 398 regions of 5\(\,\times \,\)5 cm, half of which contained a malignant lesion and the other half depicted suspicious regions in normal mammograms detected by a traditional CAD system. Four radiologists participated in the study. ROC analysis was used for evaluating results. The AUC of CNN was 0.87, which was higher than the mean AUC of the radiologists (0.84), though the difference was not significant.

Keywords

Convolutional Neural Network Soft Tissue Lesion Stochastic Gradient Descent Convolutional Layer Deep Convolutional Neural Network 
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.

Notes

Acknowledgements

This research was funded by grant KUN 2012-5577 of the Dutch Cancer Society and supported by the Foundation of Population Screening Mid West.

References

  1. 1.
    Fenton, J.J., Abraham, L., Taplin, S.H., Geller, B.M., Carney, P.A., D’Orsi, C., Elmore, J.G., Barlow, W.E.: Effectiveness of computer-aided detection in community mammography practice. J. Natl. Cancer Inst. 103, 1152–1161 (2011)CrossRefGoogle Scholar
  2. 2.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)CrossRefGoogle Scholar
  5. 5.
    Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)CrossRefGoogle Scholar
  6. 6.
    Karssemeijer, N., te Brake, G.M.: Detection of stellate distortions in mammograms. IEEE Trans. Med. Imaging 15, 611–619 (1996)CrossRefGoogle Scholar
  7. 7.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:14091556
  9. 9.
    Dauphin, Y.N., de Vries, H., Chung, J., Bengio, Y.: RMSProp and equilibrated adaptive learning rates for non-convex optimization (2015). arXiv:150204390
  10. 10.
    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)MathSciNetMATHGoogle Scholar
  11. 11.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015). arXiv:150201852v1
  12. 12.
    Hillis, S.L., Berbaum, K.S., Metz, C.E.: Recent developments in the Dorfman-Berbaum-Metz procedure for multireader ROC study analysis. Acad. Radiol. 15, 647–661 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Thijs Kooi
    • 1
  • Albert Gubern-Merida
    • 1
  • Jan-Jurre Mordang
    • 1
  • Ritse Mann
    • 1
  • Ruud Pijnappel
    • 2
  • Klaas Schuur
    • 2
  • Ard den Heeten
    • 3
  • Nico Karssemeijer
    • 1
  1. 1.Department of RadiologyRadboud University Medical CenterNijmegenThe Netherlands
  2. 2.Dutch Reference Centre for ScreeningNijmegenThe Netherlands
  3. 3.Department of RadiologyUniversity of AmsterdamAmsterdamThe Netherlands

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