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Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is especially important for medical image segmentation which typically requires intensive pixel/voxel-wise labeling by domain experts.
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Y. Zhang, Q. Liao, L. Ding, and J. Zhang, “Bridging 2d and 3d segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5 d solutions,” Computerized Medical Imaging and Graphics , 2022
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X. Zhao, Z. Shen, D. Chen, S. Wang, Z. Zhuang, Q. Wang, and L. Zhang, “One-shot traumatic brain segmentation with adversarial training and uncertainty rectification,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2023, pp. 120–129
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R. Jiao, Y. Zhang, L. Ding, B. Xue, J. Zhang, R. Cai, and C. Jin, “Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation,” Computers in Biology and Medicine , 2023
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Y. Zhang, Z. Shen, and R. Jiao, “Segment anything model for medical image segmentation: Current applications and future directions,” Computers in Biology and Medicine , vol. 171, p. 108238, 2024
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