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.
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References
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
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)
Eric, T., Judy Hoffman, N.Z., Darrell., T.: Deep domain confusion: maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)
French, G., Mackiewicz, M., Fisher, M.H.: Self-ensembling for domain adaptation. CoRR abs/1706.05208 (2017)
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: ICML (2016)
Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. CoRR abs/1711.03213 (2017)
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
Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. CoRR abs/1610.02242 (2016). http://arxiv.org/abs/1610.02242
Li, Y., Wang, N., Shi, J., Liu, J., Hou, X.: Revisiting batch normalization for practical domain adaptation. CoRR abs/1603.04779 (2016)
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)
Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
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
Prados, F., et al.: Spinal cord grey matter segmentation challenge. NeuroImage 152, 312–329 (2017)
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_28
Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. arXiv preprint arXiv:1712.02560 (2017)
Sankaranarayanan, S., Balaji, Y., Castillo, C.D., Chellappa, R.: Generate to adapt: aligning domains using generative adversarial networks. CoRR 1704.01705 (2017)
Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. CoRR abs/1607.01719 (2016). http://arxiv.org/abs/1607.01719
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)
Wilson, G., Cook, D.J.: Adversarial transfer learning. arXiv, vol. 1812, p. 02849 (2018)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1–35 (2016)
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Shanis, Z., Gerber, S., Gao, M., Enquobahrie, A. (2019). Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training. In: Wang, Q., et al. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. DART MIL3ID 2019 2019. Lecture Notes in Computer Science(), vol 11795. Springer, Cham. https://doi.org/10.1007/978-3-030-33391-1_4
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DOI: https://doi.org/10.1007/978-3-030-33391-1_4
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