Abstract
Domain shift is a big challenge when deploying deep learning models in real-world applications due to various data distributions. The recent advances of domain adaptation mainly come from explicitly learning domain invariant features (e.g., by adversarial learning, metric learning and self-training). While they cannot be easily extended to multi-domains due to the diverse domain knowledge. In this paper, we present a novel multi-target domain adaptation (MTDA) algorithm, i.e., prompt-DA, through implicit feature adaptation for medical image segmentation. In particular, we build a feature transfer module by simply obtaining the domain-specific prompts and utilizing them to generate the domain-aware image features via a specially designed simple feature fusion module. Moreover, the proposed prompt-DA is compatible with the previous DA methods (e.g., adversarial learning based) and the performance can be continuously improved. The proposed method is evaluated on two challenging domain-shift datasets, i.e., the Iseg2019 (domain shift in infant MRI of different ages), and the BraTS2018 dataset (domain shift between high-grade and low-grade gliomas). Experimental results indicate our proposed method achieves state-of-the-art performance in both cases, and also demonstrates the effectiveness of the proposed prompt-DA. The experiments with adversarial learning DA show our proposed prompt-DA can go well with other DA methods. Our code is available at https://github.com/MurasakiLin/prompt-DA.
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References
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
Zhou, S., Nie, D., Adeli, E., Yin, J., Lian, J., Shen, D.: High-resolution encoder-decoder networks for low-contrast medical image segmentation. IEEE Trans. Image Process. 29, 461–475 (2019)
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
Xie, X., Chen, J., Li, Y., Shen, L., Ma, K., Zheng, Y.: MI\(^2\)GAN: generative adversarial network for medical image domain adaptation using mutual information constraint. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 516–525. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59713-9_50
Dou, Q., et al.: Pnp-adanet: plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation. IEEE Access 7, 99 065–99 076 (2019)
Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.-A.: Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 865–872 (2019)
Cui, H., Yuwen, C., Jiang, L., Xia, Y., Zhang, Y.: Bidirectional cross-modality unsupervised domain adaptation using generative adversarial networks for cardiac image segmentation. Comput. Biol. Med. 136, 104726 (2021)
Kumar, A., Ma, T., Liang, P.: Understanding self-training for gradual domain adaptation. In: International Conference on Machine Learning, pp. 5468–5479. PMLR (2020)
Sheikh, R., Schultz, T.: Unsupervised domain adaptation for medical image segmentation via self-training of early features. In: International Conference on Medical Imaging with Deep Learning, pp. 1096–1107. PMLR (2022)
Xie, Q., et al.: Unsupervised domain adaptation for medical image segmentation by disentanglement learning and self-training. IEEE Trans. Med. Imaging (2022)
Yang, C., Guo, X., Chen, Z., Yuan, Y.: Source free domain adaptation for medical image segmentation with Fourier style mining. Med. Image Anal. 79, 102457 (2022)
Liu, X., Ji, K., Fu, Y., Du, Z., Yang, Z., Tang, J.: P-tuning v2: prompt tuning can be comparable to fine-tuning universally across scales and tasks. arXiv preprint arXiv:2110.07602 (2021)
Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. Int. J. Comput. Vision 130(9), 2337–2348 (2022)
Jia, M., et al.: Visual prompt tuning. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022. Lecture Notes in Computer Science, vol. 13693, pp. 709–727. Springer, Cham (2022)
Zheng, Z., Yue, X., Wang, K., You, Y.: Prompt vision transformer for domain generalization. arXiv preprint arXiv:2208.08914 (2022)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
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)
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_28
Sun, Y., et al.: Multi-site infant brain segmentation algorithms: the ISEG-2019 challenge. IEEE Trans. Med. Imaging 40(5), 1363–1376 (2021)
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)
Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. In: International Conference on Machine Learning, pp. 1989–1998. PMLR (2018)
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 Trans. Med. Imaging 39(7), 2494–2505 (2020)
Zeng, G., et al.: Semantic consistent unsupervised domain adaptation for cross-modality medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 201–210. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87199-4_19
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 62001222), the China Postdoctoral Science Foundation funded project (No. 2021TQ0150 and No. 2021M701699).
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Lin, Y., Nie, D., Liu, Y., Yang, M., Zhang, D., Wen, X. (2023). Multi-Target Domain Adaptation with Prompt Learning for Medical Image Segmentation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_68
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