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K. Han, L. Liu, Y. Song, Y. Liu, C. Qiu, Y. Tang, Q. Teng, and Z. Liu, “An effective semi-supervised approach for liver ct image segmentation,” IEEE Journal of Biomedical and Health Informatics , vol. 26, no. 8, pp. 3999–4007, 2022
2022
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Z. Xu, Y. Wang, D. Lu, L. Yu, J. Yan, J. Luo, K. Ma, Y. Zheng, and R. K.-y. Tong, “All-around real label supervision: Cyclic prototype consistency learning for semi-supervised medical image segmentation,” IEEE Journal of Biomedical and Health Informatics , vol. 26, no. 7, pp. 3174–3184, 2022
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Z. Zhang, C. Tian, H. X. Bai, Z. Jiao, and X. Tian, “Discriminative error prediction network for semi-supervised colon gland segmentation,” Medical Image Analysis , vol. 79, p. 102458, 2022
2022
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M. Van Waerebeke, G. Lodygensky, and J. Dolz, “On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentation,” in International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging . Springer, 2022, pp. 36–46
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X. Zhao, C. Fang, D.-J. Fan, X. Lin, F. Gao, and G. Li, “Cross-level contrastive learning and consistency constraint for semi-supervised medical image segmentation,” in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2022, pp. 1–5
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2022
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H. Huang, Q. Chen, L. Lin, M. Cai, Q. Zhang, Y. Iwamoto, X. Han, A. Furukawa, S. Kanasaki, Y.-W. Chen, R. Tong, and H. Hu, “Mtl-abs3net: Atlas-based semi-supervised organ segmentation network with multi-task learning for medical images,” IEEE Journal of Biomedical and Health Informatics , vol. 26, no. 8, pp. 3988–3998, 2022
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L. Hu, J. Li, X. Peng, J. Xiao, B. Zhan, C. Zu, X. Wu, J. Zhou, and Y. Wang, “Semi-supervised npc segmentation with uncertainty and attention guided consistency,” Knowledge-Based Systems , vol. 239, p. 108021, 2022
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B. H. Thompson, G. Di Caterina, and J. P. Voisey, “Pseudo-label refinement using superpixels for semi-supervised brain tumour segmentation,” in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2022, pp. 1–5
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A. Xu, S. Wang, S. Ye, J. Fan, X. Shi, and X. Xia, “Ca-mt: A self-ensembling model for semi-supervised cardiac segmentation with elliptical descriptor based contour-aware,” in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2022, pp. 1–5
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C. M. Seibold, S. Reiß, J. Kleesiek, and R. Stiefelhagen, “Reference-guided pseudo-label generation for medical semantic segmentation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 2, 2022, pp. 2171–2179
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K. Zheng, J. Xu, and J. Wei, “Double noise mean teacher self-ensembling model for semi-supervised tumor segmentation,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 1446–1450
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M.-C. Xu, Y.-K. Zhou, C. Jin, S. B. Blumberg, F. J. Wilson, M. deGroot, D. C. Alexander, N. P. Oxtoby, and J. Jacob, “Learning morphological feature perturbations for calibrated semi-supervised segmentation,” in International Conference on Medical Imaging with Deep Learning . PMLR, 2022, pp. 1413–1429
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W. Huang, C. Chen, Z. Xiong, Y. Zhang, X. Chen, X. Sun, and F. Wu, “Semi-supervised neuron segmentation via reinforced consistency learning,” IEEE Transactions on Medical Imaging , pp. 1–1, 2022
2022
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C. Chen, K. Zhou, Z. Wang, and R. Xiao, “Generative consistency for semi-supervised cerebrovascular segmentation from tof-mra,” IEEE Transactions on Medical Imaging , pp. 1–1, 2022
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Y. Liu, W. Wang, G. Luo, K. Wang, and S. Li, “A contrastive consistency semi-supervised left atrium segmentation model,” Computerized Medical Imaging and Graphics , p. 102092, 2022
2022
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X. Chen, H.-Y. Zhou, F. Liu, J. Guo, L. Wang, and Y. Yu, “Mass: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired ct and mri images,” Medical Image Analysis , p. 102506, 2022
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J. Wang and T. Lukasiewicz, “Rethinking bayesian deep learning methods for semi-supervised volumetric medical image segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 182–190
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H. Wu, Z. Wang, Y. Song, L. Yang, and J. Qin, “Cross-patch dense contrastive learning for semi-supervised segmentation of cellular nuclei in histopathologic images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 11 666–11 675
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X. Luo, G. Wang, W. Liao, J. Chen, T. Song, Y. Chen, S. Zhang, D. N. Metaxas, and S. Zhang, “Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency,” Medical Image Analysis , p. 102517, 2022
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Y. Lin, H. Yao, Z. Li, G. Zheng, and X. Li, “Calibrating label distribution for class-imbalanced barely-supervised knee segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2022, pp. 109–118
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H. Wu, J. Liu, F. Xiao, Z. Wen, L. Cheng, and J. Qin, “Semi-supervised segmentation of echocardiography videos via noise-resilient spatiotemporal semantic calibration and fusion,” Medical Image Analysis , vol. 78, p. 102397, 2022
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X. Wang, Y. Yuan, D. Guo, X. Huang, Y. Cui, M. Xia, Z. Wang, C. Bai, and S. Chen, “Ssa-net: Spatial self-attention network for covid-19 pneumonia infection segmentation with semi-supervised few-shot learning,” Medical Image Analysis , vol. 79, p. 102459, 2022
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Z. Fang, J. Bai, X. Guo, X. Wang, F. Gao, H.-Y. Yang, B. Kong, Y. Hou, K. Cao, Q. Song et al. , “Annotation-efficient covid-19 pneumonia lesion segmentation using error-aware unified semi-supervised and active learning,” IEEE Transactions on Artificial Intelligence , 2022
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Z. Li, Z. Li, R. Liu, Z. Luo, and X. Fan, “Coupling deep deformable registration with contextual refinement for semi-supervised medical image segmentation,” in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2022, pp. 1–5
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Y. Shu, H. Li, B. Xiao, X. Bi, and W. Li, “Cross-mix monitoring for medical image segmentation with limited supervision,” IEEE Transactions on Multimedia , pp. 1–1, 2022
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Q.-Q. Chen, Z.-H. Sun, C.-F. Wei, E. Q. Wu, and D. Ming, “Semi-supervised 3d medical image segmentation based on dual-task consistent joint learning and task-level regularization,” IEEE/ACM Transactions on Computational Biology and Bioinformatics , pp. 1–1, 2022
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M. Liu, L. Xiao, H. Jiang, and Q. He, “Ccat-net: A novel transformer based semi-supervised framework for covid-19 lung lesion segmentation,” in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2022, pp. 1–5
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J. Yang, Y. Tao, Q. Xu, Y. Zhang, X. Ma, S. Yuan, and Q. Chen, “Self-supervised sequence recovery for semi-supervised retinal layer segmentation,” IEEE Journal of Biomedical and Health Informatics , vol. 26, no. 8, pp. 3872–3883, 2022
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H. He, A. Banerjee, M. Beetz, R. P. Choudhury, and V. Grau, “Semi-supervised coronary vessels segmentation from invasive coronary angiography with connectivity-preserving loss function,” in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) , 2022, pp. 1–5
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Y. Wu, Z. Ge, D. Zhang, M. Xu, L. Zhang, Y. Xia, and J. Cai, “Mutual consistency learning for semi-supervised medical image segmentation,” Medical Image Analysis , p. 102530, 2022
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J. Wang, X. Li, Y. Han, J. Qin, L. Wang, and Z. Qichao, “Separated contrastive learning for organ-at-risk and gross-tumor-volume segmentation with limited annotation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 3, 2022, pp. 2459–2467
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X. Luo, M. Hu, T. Song, G. Wang, and S. Zhang, “Semi-supervised medical image segmentation via cross teaching between cnn and transformer,” in Medical Imaging With Deep Learning . PMLR, 2022, pp. 1–14
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Z. Zhao, J. Hu, Z. Zeng, X. Yang, P. Qian, B. Veeravalli, and C. Guan, “Mmgl: Multi-scale multi-view global-local contrastive learning for semi-supervised cardiac image segmentation,” in 2022 IEEE international conference on image processing (ICIP) . IEEE, 2022, pp. 401–405
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K. Zhang and X. Zhuang, “Cyclemix: A holistic strategy for medical image segmentation from scribble supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 11 656–11 665
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J. Hou, X. Ding, and J. D. Deng, “Semi-supervised semantic segmentation of vessel images using leaking perturbations,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2022, pp. 2625–2634
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X. Yang, Z. Song, I. King, and Z. Xu, “A survey on deep semi-supervised learning,” IEEE Transactions on Knowledge and Data Engineering , 2022
2022
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J. Kim, Y. Min, D. Kim, G. Lee, J. Seo, K. Ryoo, and S. Kim, “Conmatch: Semi-supervised learning with confidence-guided consistency regularization,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXX . Springer, 2022, pp. 674–690
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Y. Lin, H. Yao, Z. Li, G. Zheng, and X. Li, “Calibrating label distribution for class-imbalanced barely-supervised knee segmentation,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 , L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, and S. Li, Eds. Cham: Springer Nature Switzerland, 2022, pp. 109–118
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Y. Zhang, R. Jiao, Q. Liao, D. Li, and J. Zhang, “Uncertainty-guided mutual consistency learning for semi-supervised medical image segmentation,” Artificial Intelligence in Medicine , vol. 138, p. 102476, 2023
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P. Bilic, P. Christ, H. B. Li, E. Vorontsov, A. Ben-Cohen, G. Kaissis, A. Szeskin, C. Jacobs, G. E. H. Mamani, G. Chartrand et al. , “The liver tumor segmentation benchmark (lits),” Medical Image Analysis , vol. 84, p. 102680, 2023
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J. Qiu, L. Li, S. Wang, K. Zhang, Y. Chen, S. Yang, and X. Zhuang, “Myops-net: Myocardial pathology segmentation with flexible combination of multi-sequence cmr images,” Medical Image Analysis , vol. 84, p. 102694, 2023
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2023
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L. Yang, L. Qi, L. Feng, W. Zhang, and Y. Shi, “Revisiting weak-to-strong consistency in semi-supervised semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 7236–7246
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N. Li, L. Xiong, W. Qiu, Y. Pan, Y. Luo, and Y. Zhang, “Segment anything model for semi-supervised medical image segmentation via selecting reliable pseudo-labels,” Available at SSRN 4477443 , 2023
2023
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H. Peiris, M. Hayat, Z. Chen, G. Egan, and M. Harandi, “Uncertainty-guided dual-views for semi-supervised volumetric medical image segmentation,” Nature Machine Intelligence , Jul 2023
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L.-L. Zeng, K. Gao, D. Hu, Z. Feng, C. Hou, P. Rong, and W. Wang, “Ss-tbn: A semi-supervised tri-branch network for covid-19 screening and lesion segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
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A. Lou, K. Tawfik, X. Yao, Z. Liu, and J. Noble, “Min-max similarity: A contrastive semi-supervised deep learning network for surgical tools segmentation,” IEEE Transactions on Medical Imaging , 2023
2023
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P. Wang, J. Peng, M. Pedersoli, Y. Zhou, C. Zhang, and C. Desrosiers, “Cat: Constrained adversarial training for anatomically-plausible semi-supervised segmentation,” IEEE Transactions on Medical Imaging , 2023
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H. Wang and X. Li, “Dhc: Dual-debiased heterogeneous co-training framework for class-imbalanced semi-supervised medical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2023, pp. 582–591
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D. Chen, Y. Bai, W. Shen, Q. Li, L. Yu, and Y. Wang, “Magicnet: Semi-supervised multi-organ segmentation via magic-cube partition and recovery,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 23 869–23 878
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