1. 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 Trans. Med. Imaging 41(5), 1043–1056 (2022)
2. Bustos, A., Pertusa, A., Salinas, J.M., de la Iglesia-Vayá, M.: PadChest: A large chest x-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797 (2020)
3. Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 9912–9924. Curran Associates, Inc. (2020). https://proceedings.neurips.cc/paper/2020/file/70feb62b69f16e0238f741fab228fec2-Paper.pdf
4. Chen, Y., et al.: Zero-shot medical image artifact reduction. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 862–866 (2020). https://doi.org/10.1109/ISBI45749.2020.9098566
5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint: arXiv:1810.04805 (2018)