Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning

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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)

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Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7167–7176 (2017)

2017

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Dou, Q., Ouyang, C., Chen, C., Chen, H., Heng, P.A.: Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. pp. 691–697 (2018)

2018

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Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp. 3–11. Springer (2018)

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Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.A.: Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE transactions on medical imaging 39

2020

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Bian, C., Yuan, C., Ma, K., Yu, S., Wei, D., Zheng, Y.: Domain adaptation meets zero-shot learning: An annotation-efficient approach to multi-modality medical image segmentation. IEEE Transactions on Medical Imaging 41

2021

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Chen, C., Liu, Q., Jin, Y., Dou, Q., Heng, P.A.: Source-free domain adaptive fundus image segmentation with denoised pseudo-labeling. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 225–235. Springer (2021)

2021

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Han, X., Qi, L., Yu, Q., Zhou, Z., Zheng, Y., Shi, Y., Gao, Y.: Deep symmetric adaptation network for cross-modality medical image segmentation. IEEE transactions on medical imaging 41

2021

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Kavur, A.E., Gezer, N.S., Barış, M., Aslan, S., Conze, P.H., Groza, V., Pham, D.D., Chatterjee, S., Ernst, P., Özkan, S., et al.: Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation. Medical Image Analysis 69

2021

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Stan, S., Rostami, M.: Privacy preserving domain adaptation for semantic segmentation of medical images. arXiv preprint arXiv:2101.00522 (2021)

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2021

Cited alongside, same era.

Bateson, M., Lombaert, H., Ben Ayed, I.: Test-time adaptation with shape moments for image segmentation. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part IV. pp. 736–745. Springer (2022)

2022

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Ding, N., Xu, Y., Tang, Y., Xu, C., Wang, Y., Tao, D.: Source-free domain adaptation via distribution estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7212–7222 (2022)

2022

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Hong, J., Zhang, Y.D., Chen, W.: Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation. Knowledge-Based Systems p. 109155 (2022)

2022

Later among the works it cites.

Liu, Y., Chen, Y., Dai, W., Gou, M., Huang, C.T., Xiong, H.: Source-free domain adaptation with contrastive domain alignment and self-supervised exploration for face anti-spoofing. In: Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XII. pp. 511–528. Springer (2022)