Advertisement

Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images

  • Jian RenEmail author
  • Ilker Hacihaliloglu
  • Eric A. Singer
  • David J. Foran
  • Xin Qi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of different tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain without requiring labeling of images at the target domain. The adaptation is achieved through adversarial training to find an invariant feature space along with the proposed Siamese architecture on the target domain to add a regularization that is appropriate for the whole-slide images. We validate the method on two prostate cancer datasets and obtain significant classification improvement of Gleason scores as compared with the baseline models.

Notes

Acknowledgment

This research was funded, in part, by grants from NIH/NCI contracts 4R01LM009239-08, 7R01CA161375-05, 1UG3CA225021-01, and P30CA072720.

References

  1. 1.
    Ferlay, J.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5), E359–E386 (2015)CrossRefGoogle Scholar
  2. 2.
    Epstein, J.I., Zelefsky, M.J., Sjoberg, D.D., et al.: A contemporary prostate cancer grading system: a validated alternative to the Gleason score. Eur. Urol. 69(3), 428–435 (2016)CrossRefGoogle Scholar
  3. 3.
    Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: CVPR, pp. 2424–2433 (2016)Google Scholar
  4. 4.
    Litjens, G., et al.: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016)CrossRefGoogle Scholar
  5. 5.
    Otálora, S., et al.: Combining unsupervised feature learning and Riesz Wavelets for histopathology image representation: application to identifying anaplastic Medulloblastoma. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 581–588. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24553-9_71CrossRefGoogle Scholar
  6. 6.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRefGoogle Scholar
  7. 7.
    Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: CVPR, vol. 1, p. 4 (2017)Google Scholar
  8. 8.
    Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1–35 (2016)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Goodfellow, I., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)Google Scholar
  10. 10.
    Kandoth, C., et al.: Mutational landscape and significance across 12 major cancer types. Nature 502(7471), 333 (2013)CrossRefGoogle Scholar
  11. 11.
    Jimenez-del Toroab, O., et al.: Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score. In: Proceedings of SPIE, vol. 10140 (2017). 101400O–1Google Scholar
  12. 12.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)Google Scholar
  13. 13.
    Fagerland, M.W., Lydersen, S., Laake, P.: The McNemar test for binary matched-pairs data: mid-p and asymptotic are better than exact conditional. BMC Med. Res. Methodol. 13(1), 91 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jian Ren
    • 1
    Email author
  • Ilker Hacihaliloglu
    • 2
  • Eric A. Singer
    • 3
  • David J. Foran
    • 3
  • Xin Qi
    • 3
  1. 1.Department of Electrical and Computer EngineeringRutgers UniversityPiscatawayUSA
  2. 2.Department of Biomedical EngineeringRutgers UniversityPiscatawayUSA
  3. 3.Rutgers Cancer Institute of New JerseyNew BrunswickUSA

Personalised recommendations