Abstract
Deep learning models perform best when tested on target (test) data domains whose distribution is similar to the set of source (train) domains. However, model generalization can be hindered when there is significant difference in the underlying statistics between the target and source domains. In this work, we adapt a domain generalization method based on a model-agnostic meta-learning framework [1] to biomedical imaging. The method learns a domain-agnostic feature representation to improve generalization of models to the unseen test distribution. The method can be used for any imaging task, as it does not depend on the underlying model architecture. We validate the approach through a computed tomography (CT) vertebrae segmentation task across healthy and pathological cases on three datasets. Next, we employ few-shot learning, i.e. training the generalized model using very few examples from the unseen domain, to quickly adapt the model to new unseen data distribution. Our results suggest that the method could help generalize models across different medical centers, image acquisition protocols, anatomies, different regions in a given scan, healthy and diseased populations across varied imaging modalities.
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References
Li, D., Yang, Y., Song, Y.Z. Hospedales, T.M.: Learning to generalize: meta-learning for domain generalization. In: Thirty-Second AAAI Conference on Artificial Intelligence, April 2018
Meng, Q., Rueckert, D., Kainz, B.: Learning cross-domain generalizable features by representation disentanglement. arXiv preprint arXiv:2003.00321 (2020)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 1–35 (2016)
Li, D., Yang, Y., Song, Y.Z. Hospedales, T.M.: Deeper, broader and artier domain generalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5542–5550 (2017)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, pp. 4077–4087 (2017)
Glocker, B., Robinson, R., Castro, D.C., Dou, Q., Konukoglu, E.: Machine learning with multi-site imaging data: an empirical study on the impact of scanner effects. arXiv preprint arXiv:1910.04597 (2019)
Kamnitsas, K., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 597–609. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_47
Valindria, V.V., et al.: Domain adaptation for MRI organ segmentation using reverse classification accuracy. arXiv preprint arXiv:1806.00363 (2018)
Ouyang, C., Kamnitsas, K., Biffi, C., Duan, J., Rueckert, D.: Data efficient unsupervised domain adaptation for cross-modality image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 669–677. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_74
Liu, Y., et al.: Cross-modality knowledge transfer for prostate segmentation from CT scans. In: Wang, Q., et al. (eds.) DART/MIL3ID -2019. LNCS, vol. 11795, pp. 63–71. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33391-1_8
Dou, Q., et al.: PnP-AdaNet: plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation. IEEE Access 7, 99065–99076 (2019)
Conjeti, S., et al.: Supervised domain adaptation of decision forests: transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization. Med. Image Anal. 32, 1–17 (2016)
Roy, A.G., Siddiqui, S., Pölsterl, S., Navab, N., Wachinger, C.: ‘Squeeze and excite’guided few-shot segmentation of volumetric images. Med. Image Anal. 59, 101587 (2020)
Zhang, L., et al.: Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans. Med. Imaging 39(7), 2531–2540 (2020)
Dou, Q., de Castro, D.C., Kamnitsas, K., Glocker, B.: Domain generalization via model-agnostic learning of semantic features. In: Advances in Neural Information Processing Systems, pp. 6450–6461 (2019)
Ilse, M., Tomczak, J.M., Louizos, C., Welling, M.: DIVA: Domain invariant variational autoencoders. arXiv preprint arXiv:1905.10427 (2019)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1126–1135. JMLR. org, August 2017
Yao, J., et al.: A multi-center milestone study of clinical vertebral CT segmentation. Comput. Med. Imaging Graph. 49, 16–28 (2016)
Korez, R., Ibragimov, B., Likar, B., Pernuš, F., Vrtovec, T.: A framework for automated spine and vertebrae interpolation-based detection and model-based segmentation. IEEE Trans. Med. Imaging 34(8), 1649–1662 (2015)
Sekuboyina, A., et al.: VerSe: a vertebrae labelling and segmentation benchmark. arXiv preprint arXiv:2001.09193 (2020)
Löffler, M.T., et al.: A vertebral segmentation dataset with fracture grading. Radiol. Artif. Intell. 2(4), e190138 (2020)
Fischl, B.: FreeSurfer. Neuroimage 62(2), 774–781 (2012)
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
Wang, G., et al.: Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans. Med. Imaging 37(7), 1562–1573 (2018)
Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A.A., Hardt, M.: Test-time training for out-of-distribution generalization. arXiv preprint arXiv:1909.13231 (2019)
Karani, N., Chaitanya, K., Konukoglu, E.: Test-time adaptable neural networks for robust medical image segmentation. arXiv preprint arXiv:2004.04668 (2020)
Zhang, J., et al.: Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction. NeuroImage 211, 116579 (2020)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8024–8035 (2019)
Crum, W.R., Camara, O., Hill, D.L.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans. Med. Imaging 25(11), 1451–1461 (2006)
Wilcoxon, F.: Individual comparisons by ranking methods. In: Breakthroughs in statistics, pp. 196–202. Springer, New York (1992). https://doi.org/10.1007/978-1-4612-4380-9_16
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This work was supported by NIH grant R01 EB017255.
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Khandelwal, P., Yushkevich, P. (2020). Domain Generalizer: A Few-Shot Meta Learning Framework for Domain Generalization in Medical Imaging. In: Albarqouni, S., et al. Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. DART DCL 2020 2020. Lecture Notes in Computer Science(), vol 12444. Springer, Cham. https://doi.org/10.1007/978-3-030-60548-3_8
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