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Self-supervised learning (SSL) has drawn increasing attention in histopathological image analysis in recent years.
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J. N. Kather, “Histological images for msi vs. mss classification in gastrointestinal cancer, ffpe samples,” feb 2019. [Online]. Available: https://doi.org/10.5281/zenodo.2530835
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M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9650–9660
2021
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P. Yang, Z. Hong, X. Yin, C. Zhu, and R. Jiang, “Self-supervised visual representation learning for histopathological images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2021, pp. 47–57
2021
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X. Wang, S. Yang, J. Zhang, M. Wang, J. Zhang, J. Huang, W. Yang, and X. Han, “Transpath: Transformer-based self-supervised learning for histopathological image classification,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2021, pp. 186–195
2021
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2020
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2021
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2021
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X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang, “Self-supervised learning: Generative or contrastive,” IEEE Transactions on Knowledge and Data Engineering , 2021
2021
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A. Taleb, C. Lippert, T. Klein, and M. Nabi, “Multimodal self-supervised learning for medical image analysis,” in International Conference on Information Processing in Medical Imaging . Springer, 2021, pp. 661–673
2021
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2021
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2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
O. Ciga, T. Xu, and A. L. Martel, “Self supervised contrastive learning for digital histopathology,” Machine Learning with Applications , vol. 7, p. 100198, 2022
2022
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