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The task of single-source domain generalization (SDG) in medical image segmentation is crucial due to frequent domain shifts in clinical image datasets.
Oppenheim, A., Lim, J.: The importance of phase in signals. Proceedings of the IEEE
1981
Earlier work this paper cites.
Decencière, et al: Feedback on a publicly distributed image database: the Messidor database. Image Analysis & Stereology
2014
Earlier work this paper cites.
Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: Proc. of Intl. Conf. on Machine Learning. pp. 1050–1059 (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? Proc. of Advances in Neural Information Processing Systems pp. 5574–5584 (2017)
2017
Earlier work this paper cites.
Almazroa, et al: Retinal fundus images for glaucoma analysis: the RIGA dataset. In: Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. vol. 10579, pp. 55–62 (2018)
2018
Earlier work this paper cites.
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. Proc. of International Conference on Learning Representations (2018)
2018
Earlier work this paper cites.
Liu, Q., et al.: Ms-net: Multi-site network for improving prostate segmentation with heterogeneous mri data. IEEE Trans. on Medical Imaging
2020
Earlier work this paper cites.
Wilson, G., Cook, D.J.: A survey of unsupervised deep domain adaptation. ACM Transactions on Intelligent Systems and Technology (TIST)
2020
Earlier work this paper cites.
Yang, Y., Lao, D., Sundaramoorthi, G., Soatto, S.: Phase consistent ecological domain adaptation. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition. pp. 9011–9020 (2020)
2020
Earlier work this paper cites.
Yang, Y., Soatto, S.: Fda: Fourier domain adaptation for semantic segmentation. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition. pp. 4085–4095 (2020)
2020
Earlier work this paper cites.
Guan, H., Liu, M.: Domain adaptation for medical image analysis: a survey. IEEE Transactions on Biomedical Engineering
2021
Cited alongside, same era.
Xie, X., Niu, J., Liu, X., Chen, Z., Tang, S., Yu, S.: A survey on incorporating domain knowledge into deep learning for medical image analysis. Medical Image Analysis
2021
Cited alongside, same era.
Xu, Q., Zhang, R., Zhang, Y., Wang, Y., Tian, Q.: A fourier-based framework for domain generalization. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition. pp. 14383–14392 (2021)
2021
Cited alongside, same era.
Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Domain generalization with mixstyle. Proc. of International Conference on Learning Representations (2021)
2021
Cited alongside, same era.
Chen, C., Li, Z., Ouyang, C., Sinclair, M., Bai, W., Rueckert, D.: Maxstyle: Adversarial style composition for robust medical image segmentation. In: Proc. of Intl. Conf. on Medical Image Computing and Computer Assisted Intervention. pp. 151–161 (2022)
Wang, J., Du, R., Chang, D., Liang, K., Ma, Z.: Domain generalization via frequency-domain-based feature disentanglement and interaction. In: Proceedings of the 30th ACM International Conference on Multimedia. pp. 4821–4829 (2022)
2022
Later among the works it cites.
Xu, Y., Xie, S., Reynolds, M., Ragoza, M., Gong, M., Batmanghelich, K.: Adversarial consistency for single domain generalization in medical image segmentation. In: Proc. of Intl. Conf. on Medical Image Computing and Computer Assisted Intervention. pp. 671–681 (2022)
2022
Later among the works it cites.
Zhang, Y., Li, M., Li, R., Jia, K., Zhang, L.: Exact feature distribution matching for arbitrary style transfer and domain generalization. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition. pp. 8035–8045 (2022)
2022
Later among the works it cites.
Chen, et al: Treasure in distribution: A domain randomization based multi-source domain generalization for 2d medical image segmentation. In: Proc. of Intl. Conf. on Medical Image Computing and Computer Assisted Intervention. pp. 89–99 (2023)
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2022
Cited alongside, same era.
Cugu, I., Mancini, M., Chen, Y., Akata, Z.: Attention consistency on visual corruptions for single-source domain generalization. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition. pp. 4165–4174 (2022)
2022
Cited alongside, same era.
Hu, et al: Domain specific convolution and high frequency reconstruction based unsupervised domain adaptation for medical image segmentation. In: Proc. of Intl. Conf. on Medical Image Computing and Computer Assisted Intervention. pp. 650–659 (2022)
2022
Cited alongside, same era.
Hu, S., Liao, Z., Zhang, J., Xia, Y.: Domain and content adaptive convolution based multi-source domain generalization for medical image segmentation. IEEE Trans. on Medical Imaging
2022
Cited alongside, same era.
Li, X., Dai, Y., Ge, Y., Liu, J., Shan, Y., Duan, L.Y.: Uncertainty modeling for out-of-distribution generalization. Proc. of International Conference on Learning Representations (2022)
2022
Cited alongside, same era.
Ouyang, et al: Causality-inspired single-source domain generalization for medical image segmentation. IEEE Trans. on Medical Imaging
2022
Cited alongside, same era.
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., Yu, P.: Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering (2022)
2022
Cited alongside, same era.
2023
Later among the works it cites.
Guo, J., Wang, N., Qi, L., Shi, Y.: ALOFT: A lightweight mlp-like architecture with dynamic low-frequency transform for domain generalization. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition. pp. 24132–24141 (2023)
2023
Later among the works it cites.
Hu, et al: Devil is in channels: Contrastive single domain generalization for medical image segmentation. In: Proc. of Intl. Conf. on Medical Image Computing and Computer Assisted Intervention. pp. 14–23 (2023)
2023
Later among the works it cites.
Li, H., et al: Frequency-mixed single-source domain generalization for medical image segmentation. In: Proc. of Intl. Conf. on Medical Image Computing and Computer Assisted Intervention. pp. 127–136 (2023)
2023
Later among the works it cites.
Su, et al: Rethinking data augmentation for single-source domain generalization in medical image segmentation. In: Proc. of the AAAI Conf. on Artificial Intelligence. vol. 37, pp. 2366–2374 (2023)
2023
Later among the works it cites.
Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nature Communications
2024
Closest in time.
Wang, H., Chen, J., Zhang, S., He, Y., Xu, J., Wu, M., He, J., Liao, W., Luo, X.: Dual-reference source-free active domain adaptation for nasopharyngeal carcinoma tumor segmentation across multiple hospitals. IEEE Trans. on Medical Imaging (2024)
2024
Closest in time.