A Preliminary Study on Breast Cancer Risk Analysis Using Deep Neural Network

  • Wenqing Sun
  • Tzu-Liang (Bill) Tseng
  • Bin Zheng
  • Wei Qian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9699)

Abstract

Deep learning is a powerful tool in computer vision areas, but it is most effective when applied to large training sets. However, large dataset are not always available for medical images. In this study we proposed a new method to use deep neural network for near-term breast cancer risk analysis. In our data base, we have 420 cases with two sequential mammogram screenings, and half of the cases were diagnosed as positive in the second screening and the other half remained negative. Instead of using human designed features, we designed a deep neural network (DNN) with four pairs of convolution neural network and one fully connected layer. Every breast image were divided into 100 ROIs with 52 by 52 pixels, and each ROI were trained with the DNN individually, and the final predictions of each case were based on the overall risk scores of all the 100 ROIs. And the ROI based area under the curve (AUC) is 0.6982, and the case based AUC is 0.7173 using our proposed scheme. The results showed our proposed scheme is promising to apply deep learning algorithms in predicting near-term breast cancer risk with limited data size.

Keywords

Breast cancer Risk analysis Deep learning Deep neural network Convolutional neural network 

References

  1. 1.
    Smith, R.A., Duffy, S., Tabar, L.: Breast cancer screening: the evolving evidence. Oncology 26(5), 471–486 (2012)Google Scholar
  2. 2.
    Amir, E., Freedman, O.C., Seruga, B., Evans, D.G.: Assessing women at high risk of breast cancer: a review of risk assessment models. J. Natl. Cancer Inst. 102(10), 680–691 (2010)CrossRefGoogle Scholar
  3. 3.
    Nelson, H.D., Tyne, K., Naik, A., Bougatsos, C., Chan, B.K., Humphrey, L.: Screening for breast cancer: an update for the U.S. preventive services task force. Ann. Intern. Med. 151(10), 727–737 (2009)CrossRefGoogle Scholar
  4. 4.
    Kopans, D.B.: Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk. Radiology 246(2), 348–353 (2008)CrossRefGoogle Scholar
  5. 5.
    Sun, W., Tseng, T.-L.B., Qian, W., Zhang, J., Saltzstein, E.C., Zheng, B., Lure, F., Yu, H., Zhou, S.: Using multiscale texture and density features for near-term breast cancer risk analysis. Med. Phys. 42(6), 2853–2862 (2015)CrossRefGoogle Scholar
  6. 6.
    Qian, W., Sun, W., Zheng, B.: Improving the efficacy of mammography screening: the potential and challenge of developing new computer-aided detection approaches. Expert Rev. Med. Devices 12(5), 497–499 (2015)CrossRefGoogle Scholar
  7. 7.
    Sun, W., Zheng, B., Lure, F., Wu, T., Zhang, J., Wang, B.Y., Saltzstein, E.C., Qian, W.: Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms. Comput. Med. Imaging Graph. 38(5), 348–357 (2014)CrossRefGoogle Scholar
  8. 8.
    Sun, W., Tseng, T.-L.B., Zheng, B., Zhang, J., Qian, W.: A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms. In: SPIE Medical Imaging, vol. 9414, p. 941422. International Society for Optics and Photonics (2015)Google Scholar
  9. 9.
    LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: ISCAS 2010 - 2010 IEEE International Symposium on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems, pp. 253–256 (2010)Google Scholar
  10. 10.
    Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, pp. 1–6 (2003)Google Scholar
  11. 11.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Wenqing Sun
    • 1
  • Tzu-Liang (Bill) Tseng
    • 1
  • Bin Zheng
    • 2
    • 3
  • Wei Qian
    • 1
    • 3
  1. 1.College of EngineeringUniversity of Texas at El PasoEl PasoUSA
  2. 2.College of EngineeringUniversity of OklahomaNormanUSA
  3. 3.College of Biological SciencesUniversity of Texas at El PasoEl PasoUSA

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