Cross-view Contrastive Mutual Learning Across Masked Autoencoders for Mammography Diagnosis

Author:

Wu Qingxia,Tan Hongna,Qiao Zhi,Dong Pei,Shen Dinggang,Wang Meiyun,Xue Zhong

Publisher

Springer Nature Switzerland

Reference23 articles.

1. Lecture Notes in Computer Science;Z Cao,2021

2. Carneiro, G., Nascimento, J., Bradley, A.P.: Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans. Med. Imaging 36(11), 2355–2365 (2017)

3. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)

4. Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners. Adv. Neural. Inf. Process. Syst. 33, 22243–22255 (2020)

5. Chen, Z., et al.: Multi-modal masked autoencoders for medical vision-and-language pre-training. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. LNCS, vol. 13435. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16443-9_65

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