Hashing with Residual Networks for Image Retrieval

  • Sailesh ConjetiEmail author
  • Abhijit Guha Roy
  • Amin Katouzian
  • Nassir Navab
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


We propose a novel deeply learnt convolutional neural network architecture for supervised hashing of medical images through residual learning, coined as Deep Residual Hashing (DRH). It offers maximal separability of classes in hashing space while preserving semantic similarities in local embedding neighborhoods. We also introduce a new optimization formulation comprising of complementary loss terms and regularizations that suit hashing objectives the best by controlling over quantization errors. We conduct extensive validations on 2,599 Chest X-ray images with co-morbidities against eight state-of-the-art hashing techniques and demonstrate improved performance and computational benefits of the proposed algorithm for fast and scalable retrieval.


  1. 1.
    Lai, H., Pan, Y., Liu, Y., Yan, S.: Simultaneous feature learning and hash coding with deep neural networks. In: CVPR 2015, pp. 3270–3278. IEEE (2015)Google Scholar
  2. 2.
    Slaney, M., Casey, M.: Locality-sensitive hashing for finding nearest neighbours. Sig. Proc. Mag. 5(2), 128–131 (2008)CrossRefGoogle Scholar
  3. 3.
    Gong, Y., Lazebnik, S.: Iterative quantization: a procrustean approach to learning binary codes. In: CVPR 2011, pp. 817–824. IEEE (2011)Google Scholar
  4. 4.
    Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F.: Supervised hashing with kernels. In: CVPR 2012, pp. 2074–2081. IEEE (2012)Google Scholar
  5. 5.
    Conjeti, S., Katouzian, A., Kazi, A., Mesbah, S., Beymer, D., Syeda-Mahmood, T.F., Navab, N.: Metric hashing forests. MedIA 34, 13–29 (2016)Google Scholar
  6. 6.
    Torralba, A., Fergus, R., Weiss, Y.: Small codes and large image databases for recognition. In: CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  7. 7.
    Zhang, X., Su, H., Yang, L., Zhang, S.: Weighted hashing with multiple cues for cell-level analysis of histopathological images. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 303–314. Springer, Cham (2015). doi: 10.1007/978-3-319-19992-4_23 CrossRefGoogle Scholar
  8. 8.
    Sze-To, A., Tizhoosh, H.R., Wong, A.K.: Binary codes for tagging x-ray images via deep de-noising autoencoders. arXiv preprint (2016). arXiv:1604.07060
  9. 9.
    Maaten, L.V., Hinton, G.: Visualizing data using t-SNE. JMLR 9, 2579–2605 (2008)zbMATHGoogle Scholar
  10. 10.
    Zhu, H., Long, M., Wang, J., Cao, Y.: Deep hashing network for efficient similarity retrieval. In: AAAI 2016 (2016)Google Scholar
  11. 11.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint (2015). arXiv:1512.03385
  12. 12.
    Goldberger, J., Hinton, G.E., Roweis, S.T., Salakhutdinov, R.: Neighbourhood components analysis. In: NIPS 2004, pp. 513–520 (2004)Google Scholar
  13. 13.
    Demner-Fushman, D., Kohli, M.D., Rosenman, M.B., Shooshan, S.E., Rodriguez, L., Antani, S., Thoma, G.R., McDonald, C.J.: Preparing a collection of radiology examinations for distribution and retrieval. JAMIA 23(2), 304–310 (2016)Google Scholar
  14. 14.
    Shin, H.C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., Summers, R.M.: Learning to read chest x-rays: recurrent neural cascade model for automated image annotation. arXiv preprint (2016). arXiv:1603.08486
  15. 15.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS 2012, pp. 1097–1105 (2012)Google Scholar
  16. 16.
    Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. arXiv preprint (2014). arXiv:1405.3531
  17. 17.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV 42(3), 145–75 (2001)CrossRefzbMATHGoogle Scholar
  18. 18.
    Vedaldi, A., Matconvnet, L.K.: Convolutional neural networks for matlab. In: ACM International Conference on Multimedia 2015, pp. 689–692. ACM (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sailesh Conjeti
    • 1
    Email author
  • Abhijit Guha Roy
    • 1
  • Amin Katouzian
    • 2
  • Nassir Navab
    • 1
    • 3
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
  2. 2.IBM Almaden Research CenterAlmadenUSA
  3. 3.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA

Personalised recommendations