Siamese Network for Dual-View Mammography Mass Matching

  • Shaked PerekEmail author
  • Alon Hazan
  • Ella Barkan
  • Ayelet Akselrod-Ballin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)


In a standard mammography screening procedure, two X-ray images are acquired per breast from two views. In this paper, we introduce a patch based, deep learning network for lesion matching in dual-view mammography using a Siamese network. Our method is evaluated on several datasets, among them the large freely available digital database for screening mammography (DDSM). We perform a comprehensive set of experiment, focusing on the mass correspondence problem. We analyze the effect of transfer learning between different types of dataset, compare the network based matching to classic template matching and evaluate the contribution of the matching network to the detection task. Experimental results show the promise in improving detection accuracy by our approach.


Biomedical imaging Deep learning Mammography 


  1. 1.
    Amit, G., Hashoul, S., Kisilev, P., Ophir, B., Walach, E., Zlotnick, A.: Automatic dual-view mass detection in full-field digital mammograms. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 44–52. Springer, Cham (2015). Scholar
  2. 2.
    Ballard, D.H., Brown, C.M.: Computer Vision, 1st edn. Prentice Hall Professional Technical Reference, New York (1982)Google Scholar
  3. 3.
    Bekker, A.J., Greenspan, H., Goldberger, J.: A multi-view deep learning architecture for classification of breast microcalcifications. In: IEEE 13th International Symposium on ISBI, pp. 726–730. IEEE (2016)Google Scholar
  4. 4.
    Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: IEEE Computer Society Conference on CVPR, vol. 1, pp. 539–546. IEEE (2005)Google Scholar
  5. 5.
    Dhungel, N., Carneiro, G., Bradley, A.P.: Fully automated classification of mammograms using deep residual neural networks. In: ISBI, pp. 310–314. IEEE (2017)Google Scholar
  6. 6.
    Geras, K.J., Wolfson, S., Shen, Y., Kim, S., Moy, L., Cho, K.: High-resolution breast cancer screening with multi-view deep convolutional neural networks. arXiv preprint arXiv:1703.07047 (2017)
  7. 7.
    Giger, M.L., Karssemeijer, N., Schnabel, J.A.: Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu. Rev. Biomed. Eng. 15, 327–357 (2013)CrossRefGoogle Scholar
  8. 8.
    Han, X., Leung, T., Jia, Y., Sukthankar, R., Berg, A.C.: MatchNet: unifying feature and metric learning for patch-based matching. In: CVPR. IEEE (2015)Google Scholar
  9. 9.
    Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.: The digital database for screening mammography. In: Digital Mammography, pp. 431–434 (2000)Google Scholar
  10. 10.
    Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2 (2015)Google Scholar
  11. 11.
    Paquerault, S., Petrick, N., Chan, H.P., Sahiner, B., Helvie, M.A.: Improvement of computerized mass detection on mammograms: fusion of two-view information. Med. Phys. 29(2), 238–247 (2002)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  14. 14.
    Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE TMI 35(5), 1299–1312 (2016)Google Scholar
  15. 15.
    Teare, P., Fishman, M., Benzaquen, O., Toledano, E., Elnekave, E.: Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. J. Digit. Imaging 30(4), 499–505 (2017)CrossRefGoogle Scholar
  16. 16.
    Wang, J., Fang, Z., Lang, N., Yuan, H., Su, M.Y., Baldi, P.: A multi-resolution approach for spinal metastasis detection using deep siamese neural networks. Comput. Biol. Med. 84, 137–146 (2017)CrossRefGoogle Scholar
  17. 17.
    Warren, R.M., Duffy, S., Bashir, S.: The value of the second view in screening mammography. Br. J. Radiol. 69(818), 105–108 (1996)CrossRefGoogle Scholar
  18. 18.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shaked Perek
    • 1
    Email author
  • Alon Hazan
    • 1
  • Ella Barkan
    • 1
  • Ayelet Akselrod-Ballin
    • 1
  1. 1.IBM ResearchHaifaIsrael

Personalised recommendations