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Deep learning-based medical image segmentation typically requires large amount of labeled data for training, making it less applicable in clinical settings due to high annotation cost.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, in: Workshop on challenges in representation learning, ICML, Atlanta. p. 896
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3d u-net: learning dense volumetric segmentation from sparse annotation, in: International conference on medical image computing and computer-assisted intervention, Springer. pp. 424–432
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O., 2016 · 2016
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A., Valpola, H., 2017 · 2017
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Deep adversarial networks for biomedical image segmentation utilizing unannotated images, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 408–416
Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z., 2017 · 2017
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Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Cheplygina, V., de Bruijne, M., Pluim, J.P., 2019 · 2019
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Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22, Springer. pp. 605–613
Yu, L., Wang, S., Li, X., Fu, C.W., Heng, P.A., 2019 · 2019
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Brats miccai brain tumor dataset
Bakas, S.S., 2020 · 2020
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Shape-aware semi-supervised 3d semantic segmentation for medical images, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 552–561
Li, S., Zhang, C., He, X., 2020 · 2020
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Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J.N., Wu, Z., Ding, X., 2020 · 2020
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Deepseg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance flair images
Zeineldin, R.A., Karar, M.E., Coburger, J., Wirtz, C.R., Burgert, O., 2020 · 2020
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Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation
DuMont Schütte, A., Hetzel, J., Gatidis, S., Hepp, T., Dietz, B., Bauer, S., Schwab, P., 2021 · 2021
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A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging
Xiong, Z., Xia, Q., Hu, Z., Huang, N., Bian, C., Zheng, Y., Vesal, S., Ravikumar, N., Maier, A., Yang, X., et al., 2021 · 2021
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Exploiting shared knowledge from non-covid lesions for annotation-efficient covid-19 ct lung infection segmentation
Zhang, Y., Liao, Q., Yuan, L., Zhu, H., Xing, J., Zhang, J., 2021 · 2021
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Dual-task mutual learning for semi-supervised medical image segmentation, in: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Springer. pp. 548–559
Zhang, Y., Zhang, J., 2021 · 2021
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The medical segmentation decathlon
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B.A., Litjens, G., Menze, B., Ronneberger, O., Summers, R.M., et al., 2022 · 2022
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Semi-supervised 3d medical image segmentation based on dual-task consistent joint learning and task-level regularization
Chen, Q.Q., Sun, Z.H., Wei, C.F., Wu, E.Q., Ming, D., 2022 · 2022
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Mtl-abs3net: Atlas-based semi-supervised organ segmentation network with multi-task learning for medical images
Huang, H., Chen, Q., Lin, L., Cai, M., Zhang, Q.W., Iwamoto, Y., Han, X., Furukawa, A., Kanasaki, S., Chen, Y.W., et al., 2022 · 2022
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Deep learning methods for automatic evaluation of delayed enhancement-mri. the results of the emidec challenge
Lalande, A., Chen, Z., Pommier, T., Decourselle, T., Qayyum, A., Salomon, M., Ginhac, D., Skandarani, Y., Boucher, A., Brahim, K., et al., 2022 · 2022
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Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency
Luo, X., Wang, G., Liao, W., Chen, J., Song, T., Chen, Y., Zhang, S., Metaxas, D.N., Zhang, S., 2022 · 2022
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Abdomenct-1k: Is abdominal organ segmentation a solved problem?
Ma, J., Zhang, Y., Gu, S., Zhu, C., Ge, C., Zhang, Y., An, X., Wang, C., Wang, Q., Liu, X., Cao, S., Zhang, Q., Liu, S., Wang, Y., Li, Y., He, J., Yang, X., 2022 · 2022
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Reference-guided pseudo-label generation for medical semantic segmentation 36, 2171–2179
Seibold, C.M., Reiß, S., Kleesiek, J., Stiefelhagen, R., 2022 · 2022
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Ssa-net: Spatial self-attention network for covid-19 pneumonia infection segmentation with semi-supervised few-shot learning
Wang, X., Yuan, Y., Guo, D., Huang, X., Cui, Y., Xia, M., Wang, Z., Bai, C., Chen, S., 2022 · 2022
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Toward foundational deep learning models for medical imaging in the new era of transformer networks
Willemink, M.J., Roth, H.R., Sandfort, V., 2022 · 2022
Cited alongside, same era.
Bridging 2d and 3d segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5 d solutions, Elsevier. p. 102088
Zhang, Y., Liao, Q., Ding, L., Zhang, J., 2022 · 2022
Track anything: Segment anything meets videos
Yang, J., Gao, M., Li, Z., Gao, S., Wang, F., Zheng, F., 2023 · 2023
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Customized segment anything model for medical image segmentation
Zhang, K., Liu, D., 2023 · 2023
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Uncertainty-guided mutual consistency learning for semi-supervised medical image segmentation
Zhang, Y., Jiao, R., Liao, Q., Li, D., Zhang, J., 2023 · 2023
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One-shot traumatic brain segmentation with adversarial training and uncertainty rectification, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 120–129
Zhao, X., Shen, Z., Chen, D., Wang, S., Zhuang, Z., Wang, Q., Zhang, L., 2023 · 2023
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Anatomically-aware uncertainty for semi-supervised image segmentation
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Cited alongside, same era.
Foundational models defining a new era in vision: A survey and outlook
Awais, M., Naseer, M., Khan, S., Anwer, R.M., Cholakkal, H., Shah, M., Yang, M.H., Khan, F.S., 2023 · 2023
Cited alongside, same era.
Magicnet: Semi-supervised multi-organ segmentation via magic-cube partition and recovery, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 23869–23878
Chen, D., Bai, Y., Shen, W., Li, Q., Yu, L., Wang, Y., 2023 · 2023
Cited alongside, same era.
Cheng, J., Ye, J., Deng, Z., Chen, J., Li, T., Wang, H., Su, Y., Huang, Z., Chen, J., Jiang, L., et al., 2023 · 2023
Cited alongside, same era.
Segvol: Universal and interactive volumetric medical image segmentation
Du, Y., Bai, F., Huang, T., Zhao, B., 2023 · 2023
Cited alongside, same era.
Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation
Jiao, R., Zhang, Y., Ding, L., Xue, B., Zhang, J., Cai, R., Jin, C., 2023 · 2023
Cited alongside, same era.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al., 2023 · 2023
Cited alongside, same era.
Semantic-sam: Segment and recognize anything at any granularity
Li, F., Zhang, H., Sun, P., Zou, X., Liu, S., Yang, J., Li, C., Zhang, L., Gao, J., 2023 · 2023
Cited alongside, same era.
Adiga, S., Dolz, J., Lombaert, H., 2024 · 2024
Later among the works it cites.
A review of the segment anything model (sam) for medical image analysis: Accomplishments and perspectives
Ali, M., Wu, T., Hu, H., Luo, Q., Xu, D., Zheng, W., Jin, N., Yang, C., Yao, J., 2024 · 2024
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Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation
Chen, C., Miao, J., Wu, D., Zhong, A., Yan, Z., Kim, S., Hu, J., Liu, Z., Sun, L., Li, X., et al., 2024 · 2024
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Unleashing the potential of sam for medical adaptation via hierarchical decoding, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3511–3522
Cheng, Z., Wei, Q., Zhu, H., Wang, Y., Qu, L., Shao, W., Zhou, Y., 2024 · 2024
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Semi-mamba-unet: Pixel-level contrastive and cross-supervised visual mamba-based unet for semi-supervised medical image segmentation
Ma, C., Wang, Z., 2024 · 2024
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Segment anything in medical images
Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B., 2024 · 2024
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Shi, Y., Ma, J., Yang, J., Wang, S., Zhang, Y., 2024 · 2024
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Sdcl: Students discrepancy-informed correction learning for semi-supervised medical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 567–577
Song, B., Wang, Q., 2024 · 2024
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On the challenges and perspectives of foundation models for medical image analysis
Zhang, S., Metaxas, D., 2024 · 2024
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Unleashing the potential of sam2 for biomedical images and videos: A survey
Zhang, Y., Shen, Z., 2024 · 2024
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Semisam: Enhancing semi-supervised medical image segmentation via sam-assisted consistency regularization, in: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE. pp. 3982–3986
Zhang, Y., Yang, J., Liu, Y., Cheng, Y., Qi, Y., 2024c · 2024
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Segmentanybone: A universal model that segments any bone at any location on mri
Gu, H., Colglazier, R., Dong, H., Zhang, J., Chen, Y., Yildiz, Z., Chen, Y., Li, L., Yang, J., Willhite, J., et al., 2025 · 2025
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Scribbleprompt: fast and flexible interactive segmentation for any biomedical image, in: European Conference on Computer Vision, Springer. pp. 207–229
Wong, H.E., Rakic, M., Guttag, J., Dalca, A.V., 2025 · 2025
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Seganypet: Universal promptable segmentation from positron emission tomography images
Zhang, Y., Xue, L., Zhang, W., Li, L., Liu, Y., Jiang, C., Cheng, Y., Qi, Y., 2025 · 2025
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