Towards Label-Efficient Deep Learning for Myopic Maculopathy Classification
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Springer Nature Switzerland
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https://link.springer.com/content/pdf/10.1007/978-3-031-54857-4_3
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2. Cai, Z., Lin, L., He, H., Tang, X.: Uni4Eye: unified 2d and 3d self-supervised pre-training via masked image modeling transformer for ophthalmic image classification. 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. 13438, pp. 88–98. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16452-1_9
3. Dai, L., et al.: A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12(1), 3242 (2021)
4. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
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