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2019 | OriginalPaper | Chapter

Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training

Authors : Zahil Shanis, Samuel Gerber, Mingchen Gao, Andinet Enquobahrie

Published in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Publisher: Springer International Publishing

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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.
Literature
1.
go back to reference Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017) CrossRef Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017) CrossRef
2.
go back to reference 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) 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)
3.
4.
go back to reference French, G., Mackiewicz, M., Fisher, M.H.: Self-ensembling for domain adaptation. CoRR abs/1706.05208 (2017) French, G., Mackiewicz, M., Fisher, M.H.: Self-ensembling for domain adaptation. CoRR abs/1706.05208 (2017)
5.
go back to reference Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: ICML (2016) Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: ICML (2016)
6.
go back to reference Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. CoRR abs/1711.03213 (2017) Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. CoRR abs/1711.03213 (2017)
9.
go back to reference Li, Y., Wang, N., Shi, J., Liu, J., Hou, X.: Revisiting batch normalization for practical domain adaptation. CoRR abs/1603.04779 (2016) Li, Y., Wang, N., Shi, J., Liu, J., Hou, X.: Revisiting batch normalization for practical domain adaptation. CoRR abs/1603.04779 (2016)
10.
go back to reference 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) 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)
11.
go back to reference Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014) CrossRef Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014) CrossRef
13.
go back to reference Prados, F., et al.: Spinal cord grey matter segmentation challenge. NeuroImage 152, 312–329 (2017) CrossRef Prados, F., et al.: Spinal cord grey matter segmentation challenge. NeuroImage 152, 312–329 (2017) CrossRef
15.
go back to reference Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. arXiv preprint arXiv:​1712.​02560 (2017) Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. arXiv preprint arXiv:​1712.​02560 (2017)
16.
go back to reference Sankaranarayanan, S., Balaji, Y., Castillo, C.D., Chellappa, R.: Generate to adapt: aligning domains using generative adversarial networks. CoRR 1704.01705 (2017) Sankaranarayanan, S., Balaji, Y., Castillo, C.D., Chellappa, R.: Generate to adapt: aligning domains using generative adversarial networks. CoRR 1704.01705 (2017)
18.
go back to reference 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) 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)
19.
go back to reference Wilson, G., Cook, D.J.: Adversarial transfer learning. arXiv, vol. 1812, p. 02849 (2018) Wilson, G., Cook, D.J.: Adversarial transfer learning. arXiv, vol. 1812, p. 02849 (2018)
20.
go back to reference Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1–35 (2016) MathSciNetMATH Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1–35 (2016) MathSciNetMATH
Metadata
Title
Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training
Authors
Zahil Shanis
Samuel Gerber
Mingchen Gao
Andinet Enquobahrie
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_4

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