Advertisement

Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training

  • Zahil Shanis
  • Samuel Gerber
  • Mingchen Gao
  • Andinet EnquobahrieEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)

Abstract

Advances in deep learning techniques have led to compelling achievements in medical image analysis. However, performance of neural network models degrades drastically if the test data is from a domain different from training data. In this paper, we present and evaluate a novel unsupervised domain adaptation (DA) framework for semantic segmentation which uses self ensembling and adversarial training methods to effectively tackle domain shift between MR images. We evaluate our method on two publicly available MRI dataset to address two different types of domain shifts: On the BraTS dataset [11] to mitigate domain shift between high grade and low grade gliomas and on the SCGM dataset [13] to tackle cross institutional domain shift. Through extensive evaluation, we show that our method achieves favorable results on both datasets.

References

  1. 1.
    Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)CrossRefGoogle Scholar
  2. 2.
    Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 95–104 (2017)Google Scholar
  3. 3.
    Eric, T., Judy Hoffman, N.Z., Darrell., T.: Deep domain confusion: maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)
  4. 4.
    French, G., Mackiewicz, M., Fisher, M.H.: Self-ensembling for domain adaptation. CoRR abs/1706.05208 (2017)Google Scholar
  5. 5.
    Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: ICML (2016)Google Scholar
  6. 6.
    Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. CoRR abs/1711.03213 (2017)Google Scholar
  7. 7.
    Kamnitsas, K., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. CoRR abs/1612.08894 (2016). http://arxiv.org/abs/1612.08894
  8. 8.
    Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. CoRR abs/1610.02242 (2016). http://arxiv.org/abs/1610.02242
  9. 9.
    Li, Y., Wang, N., Shi, J., Liu, J., Hou, X.: Revisiting batch normalization for practical domain adaptation. CoRR abs/1603.04779 (2016)Google Scholar
  10. 10.
    Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: On ICML, ICML 2015, vol. 37, pp. 97–105 (2015)Google Scholar
  11. 11.
    Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)CrossRefGoogle Scholar
  12. 12.
    Perone, C.S., Ballester, P., Barros, R.C., Cohen-Adad, J.: Unsupervised domain adaptation for medical imaging segmentation with self-ensembling. CoRR abs/1811.06042 (2018). http://arxiv.org/abs/1811.06042
  13. 13.
    Prados, F., et al.: Spinal cord grey matter segmentation challenge. NeuroImage 152, 312–329 (2017)CrossRefGoogle Scholar
  14. 14.
    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).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  15. 15.
    Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. arXiv preprint arXiv:1712.02560 (2017)
  16. 16.
    Sankaranarayanan, S., Balaji, Y., Castillo, C.D., Chellappa, R.: Generate to adapt: aligning domains using generative adversarial networks. CoRR 1704.01705 (2017)Google Scholar
  17. 17.
    Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. CoRR abs/1607.01719 (2016). http://arxiv.org/abs/1607.01719
  18. 18.
    Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30, pp. 1195–1204 (2017)Google Scholar
  19. 19.
    Wilson, G., Cook, D.J.: Adversarial transfer learning. arXiv, vol. 1812, p. 02849 (2018)Google Scholar
  20. 20.
    Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1–35 (2016)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zahil Shanis
    • 1
    • 2
  • Samuel Gerber
    • 1
  • Mingchen Gao
    • 2
  • Andinet Enquobahrie
    • 1
    Email author
  1. 1.Kitware Inc.CarrboroUSA
  2. 2.SUNY at BuffaloBuffaloUSA

Personalised recommendations