Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation
Abstract
Medical image segmentation is crucial in clinical diagnosis, helping physicians identify and analyze medical conditions. However, this task is often accompanied by challenges like sensitive data, privacy concerns, and expensive annotations. Current research focuses on personalized collaborative training of medical segmentation systems, ignoring that obtaining segmentation annotations is time-consuming and laborious. Achieving a perfect balance between annotation cost and segmentation performance while ensuring local model personalization has become a valuable direction. Therefore, this study introduces a novel Model-Heterogeneous Semi-Supervised Federated (HSSF) Learning framework. It proposes Regularity Condensation and Regularity Fusion to transfer autonomously selective knowledge to ensure the personalization between sites. In addition, to efficiently utilize unlabeled data and reduce the annotation burden, it proposes a Self-Assessment (SA) module and a Reliable Pseudo-Label Generation (RPG) module. The SA module generates self-assessment confidence in real-time based on model performance, and the RPG module generates reliable pseudo-label based on SA confidence. We evaluate our model separately on the Skin Lesion and Polyp Lesion datasets. The results show that our model performs better than other methods characterized by heterogeneity. Moreover, it exhibits highly commendable performance even in homogeneous designs, most notably in region-based metrics. The full range of resources can be readily accessed through the designated repository located at HSSF(github.com) on the platform of GitHub.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Federated learning presents a viable solution for leveraging multimodal data from multiple health care institutions without the need to share the actual data, thus maintaining patient privacy.
“In addition, federated learning presents a viable solution for leveraging multimodal data from multiple health care institutions without the need to share the actual data, thus maintaining patient privacy [152-156].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [155]
Federated learning techniques enable M-LLMs to be trained on decentralized data sources without the need to transfer sensitive or private information to a central location, thereby preserving data privacy and security.
“One of these measures is the implementation of federated learning techniques [153,155,156] to enable M-LLMs to be trained on decentralized data sources without the need to transfer sensitive or private information to a central location, thereby preserving data privacy and security.”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [155]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · arXiv · doi:10.48550/arxiv.1802.02611 · Cited by 20,063 · see citing works
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054 · see citing works
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works
Tarvainen, A., & Valpola, H. (2017) · arXiv · doi:10.48550/arxiv.1703.01780 · Cited by 4,275 · see citing works
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021) · arXiv · doi:10.48550/arxiv.2102.04306 · Cited by 4,206 · cited 1× · see citing works
“D EEP learning has made remarkable strides in lesion segmentation [1], [2], organ segmentation [3], [4], instrument segmentation [5], [6], etc.”
BackgroundFixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Sohn, K., Berthelot, D., Li, C.-L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., & Raffel, C. (2020) · arXiv · doi:10.48550/arxiv.2001.07685 · Cited by 2,892 · see citing works
Understanding deep learning (still) requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2021) · Communications of the ACM · doi:10.1145/3446776 · Cited by 2,400 · see citing works
Self-Training With Noisy Student Improves ImageNet Classification
Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01070 · Cited by 2,393 · see citing works
Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., & Vilariño, F. (2015) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2015.02.007 · Cited by 2,191 · see citing works
Kvasir-SEG: A Segmented Polyp Dataset
Jha, D., Smedsrud, P., Riegler, M., Halvorsen, P., de Lange, T., Johansen, D., & Johansen, H. (2019) · Conference on Multimedia Modeling · doi:10.1007/978-3-030-37734-2_37 · Cited by 2,145 · see citing works
Unsupervised Data Augmentation for Consistency TrainingPreprint
Xie, Q., Dai, Z., Hovy, E. H., Luong, T., & Le, Q. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.12848 · Cited by 1,962 · see citing works
Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer
Silva, J. C., Histace, A., Romain, O., Dray, X., & Granado, B. (2013) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-013-0926-3 · Cited by 1,318 · see citing works
Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information
Tajbakhsh, N., Gurudu, S., & Liang, J. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2487997 · Cited by 1,215 · see citing works
(2011) · Princeton University Press eBooks · doi:10.2307/j.ctvcm4g18.8 · Cited by 1,197 · see citing works
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
Vázquez, D., Bernal, J., Sánchez, F. J., Fernández‐Esparrach, G., López, A. M., Romero, A., Drozdzal, M., & Courville, A. C. (2017) · Journal of Healthcare Engineering · doi:10.1155/2017/4037190 · Cited by 868 · see citing works
FedProto: Federated Prototype Learning across Heterogeneous Clients
Tan, Y., Long, G., Liu, L., Zhou, T., Lu, Q., Jiang, J. B., & Zhang, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20819 · Cited by 809 · see citing works
Ensemble Distillation for Robust Model Fusion in Federated LearningPreprint
Lin, T., Kong, L., Stich, S. U., & Jaggi, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07242 · Cited by 733 · see citing works
Federated Learning with Matched AveragingPreprint
Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., & Khazaeni, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.06440 · Cited by 725 · see citing works
FedBN: Federated Learning on Non-IID Features via Local Batch NormalizationPreprint
Li, X., Jiang, M., Zhang, X., Kamp, M., & Dou, Q. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.07623 · Cited by 700 · see citing works
Pham, H., Dai, Z., Xie, Q., & Le, Q. V. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01139 · Cited by 643 · see citing works
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
Yang, L., Qi, L., Feng, L., Zhang, W., & Shi, Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00699 · Cited by 621 · see citing works
Federated Learning with Personalization LayersPreprint
Arivazhagan, M. G., Aggarwal, V., Singh, A. K., & Choudhary, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00818 · Cited by 595 · see citing works
FedMD: Heterogenous Federated Learning via Model DistillationPreprint
Li, D., & Wang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.03581 · Cited by 562 · see citing works
Self-citationST++: Make Self-trainingWork Better for Semi-supervised Semantic Segmentation
Yang, L., Zhuo, W., Qi, L., Shi, Y., & Gao, Y. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00423 · Cited by 555 · see citing works
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous ClientsPreprint
Diao, E., Ding, J., & Tarokh, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01264 · Cited by 390 · see citing works
CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
Li, J., Xiong, C., & Hoi, S. C. H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00934 · Cited by 301 · see citing works
Robust Federated Learning with Noisy and Heterogeneous Clients
Fang, X., & Ye, M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00983 · Cited by 208 · see citing works
Boundary-Aware Transformers for Skin Lesion Segmentation
Wang, J., Wei, L., Wang, L.-S., Zhou, Q., Zhu, L., & Qin, J. (2021) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-030-87193-2_20 · Cited by 204 · see citing works
Self-citation2017 Robotic Instrument Segmentation ChallengePreprint
Allan, M., Shvets, A. A., Kurmann, T., Zhang, Z., Duggal, R., Su, Y.-H., Rieke, N., Laina, I., Kalavakonda, N., Bodenstedt, S., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06426 · Cited by 175 · see citing works
Self-citationFederated Knowledge Distillation
Eldar, Y. C., Goldsmith, A. J., Gündüz, D., & Poor, H. V. (2022) · Cambridge University Press eBooks · doi:10.1017/9781108966559.019 · Cited by 146 · see citing works
Semi-supervised Semantic Segmentation with Error Localization Network
Kwon, D., & Kwak, S. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00972 · Cited by 126 · see citing works
Semi-supervised Segmentation Based on Error-Correcting Supervision
Mendel, R., de Souza, L. A., Jr., Rauber, D., Papa, J. P., & Palm, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_9 · Cited by 125 · see citing works
XBound-Former: Toward Cross-Scale Boundary Modeling in Transformers
Wang, J., Chen, F., Ma, Y.-X., Wang, L.-S., Fei, Z.-D., Shuai, J.-W., Tang, X., Zhou, Q., & Qin, J. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3236037 · Cited by 122 · see citing works
Self-citationParameterized Knowledge Transfer for Personalized Federated LearningPreprint
Zhang, J., Guo, S., Ma, X., Wang, H., Xu, W., & Wu, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.02862 · Cited by 122 · see citing works
RSCFed: Random Sampling Consensus Federated Semi-supervised Learning
Liang, X., Lin, Y., Fu, H., Zhu, L., & Li, X. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00991 · Cited by 108 · see citing works
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint LearningPreprint
Jeong, W., Yoon, J., Yang, E., & Hwang, S. J. (2020) · arXiv (Cornell University) · Cited by 108 · see citing works
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models
Zhang, Z. J., Yang, Y. L., Yao, Z., Yan, Y., Gonzalez, J. E., Ramchandran, K., & Mahoney, M. W. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671693 · Cited by 104 · see citing works
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
Diao, E., Ding, J., & Tarokh, V. (2022) · neural information processing systems · doi:10.52202/068431-1299 · Cited by 83 · see citing works
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and ProspectivePreprint
Jin, Y., Wei, X., Liu, Y., & Yang, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11545 · Cited by 70 · see citing works
A Knowledge Transfer-based Semi-Supervised Federated Learning for IoT Malware Detection
Pei, X., Deng, X., Tian, S., Zhang, L., & Xue, K. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3173664 · Cited by 70 · see citing works
Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning
Li, M., Li, Q., & Wang, Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01563 · Cited by 63 · see citing works
Robust Semisupervised Federated Learning for Images Automatic Recognition in Internet of Drones
Zhang, Z., Ma, S., Yang, Z., Xiong, Z., Kang, J., Wu, Y., Zhang, K., & Niyato, D. T. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3151945 · Cited by 61 · see citing works
Personalizing Federated Medical Image Segmentation via Local Calibration
Wang, J., Jin, Y., & Wang, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19803-8_27 · Cited by 39 · see citing works
Self-citationDynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance
Jiang, M., Yang, H., Li, X., Liu, Q., Heng, P., & Dou, Q. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_19 · Cited by 31 · see citing works
Semi-supervised Learning with Multi-Head Co-Training
Chen, M., Du, Y., Zhang, Y., Qian, S., & Wang, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i6.20577 · Cited by 29 · see citing works
Private Semi-Supervised Federated Learning
Fan, C., Hu, J., & Huang, J. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/279 · Cited by 23 · see citing works
Efficient Global-Local Memory for Real-Time Instrument Segmentation of Robotic Surgical Video
Wang, J., Jin, Y., Wang, L., Cai, S., Heng, P.-A., & Qin, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87202-1_33 · Cited by 16 · see citing works
Self-citationCombining Self-training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation
Liu, W., Xu, W., Yan, S., Wang, L., Li, H., & Yang, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23911-3_25 · Cited by 9 · see citing works
Self-citation
Flagged first, then by citations
1 of the 51 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024) · Journal of medical Internet research · doi:10.2196/59505 · Cited by 349 · see citing works
“One of these measures is the implementation of federated learning techniques [153,155,156] to enable M-LLMs to be trained on decentralized data sources without the need to transfer sensitive or private information to a central location, thereby preserving data privacy and security.”
Colorectal Polyp Segmentation Based on Deep Learning Methods: A Systematic Review
Liu, X., Isa, N. A. M., Chen, C., & Lv, F. (2025) · Journal of Imaging · doi:10.3390/jimaging11090293 · Cited by 20 · see citing works
A Review on Medical Image Segmentation: Datasets, Technical Models, Challenges and Solutions
Gan, H., Ramlee, M. H., Wang, Z., & Shimizu, A. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1574 · Cited by 17 · see citing works
Zhang, Y., Ma, D., & Wu, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129262 · Cited by 10 · see citing works
Federated lung nodule segmentation using a hybrid transformer–U-Net architecture
Turjya, S. M., & Fawakherji, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-35243-9 · Cited by 7 · see citing works
Chen, Y., Zeng, X., Cai, H., Xu, Q. Y., & Liu, Z. (2025) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2025.3594694 · Cited by 7 · see citing works
Effective test-time personalization for federated semantic segmentation
Chen, H., Xu, Y., Xu, Y., Zhao, Y., Zhang, Y., & Cui, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126642 · Cited by 6 · see citing works
Federated Learning for Medical Image Analysis: Privacy-Preserving Paradigms and Clinical Challenges
Hu, J., Yang, Z., Wang, P., Zhao, G., Huang, H., Zong, Z., & Wu, D. O. (2025) · Transactions on Artificial Intelligence · doi:10.53941/tai.2025.100010 · Cited by 5 · see citing works
Learning with less: A survey of deep learning in medical imaging under varying supervision levels
Kumari, S., & Singh, P. P. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103375 · Cited by 6 · see citing works
Learn the global prompt in the low-rank tensor space for heterogeneous federated learning
Fu, L., Huang, S., Li, Y., Chen, C., Zhang, C., & Zheng, Z. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107319 · Cited by 4 · see citing works
FUSION: Uncertainty‐Guided Federated Semi‐Supervised Learning for Medical Image Segmentation
Raheem, A., Yang, Z., Yu, H., Manan, M. A., Sabah, F., & Ahmed, S. (2025) · IET Image Processing · doi:10.1049/ipr2.70147 · Cited by 3 · see citing works
Uncertainty-Guided Adaptive Correction for Semi-Supervised Medical Image Segmentation
Chen, X., Tong, L., Zhao, H., & Du, B. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3636145 · Cited by 3 · see citing works
FedSemiDG: Domain generalized federated semi-supervised medical image segmentation
Deng, Z., Xu, Z., Isshiki, T., & Zheng, Y. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104096 · Cited by 3 · see citing works
Zhang, G., Lu, J., Chen, Y., Deng, Y., Zhao, B., Chen, H., & Xue, L. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107882 · Cited by 3 · see citing works
PathFL: Multi-alignment Federated Learning for pathology image segmentation
Zhang, Y., Chen, F., Qi, Y., Yang, G., & Fu, H. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103670 · Cited by 2 · see citing works
Zhang, M., Huang, T., Chen, W., Sun, X., & Xi, X. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae3272 · Cited by 2 · see citing works
Alkılınç, A., Okay, F. Y., Kök, İ., & Özdemi̇r, S. (2026) · Cluster Computing · doi:10.1007/s10586-026-06028-6 · Cited by 2 · see citing works
Stelter, L., Corbetta, V., Beets‐Tan, R. G. H., & Silva, W. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782682 · Cited by 2 · see citing works
Wang, L., Pang, S., Zhao, Z., He, X., Zhang, K., Gui, H., & Wang, N. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113921 · Cited by 2 · see citing works
Self-citationLiang, P., Chen, J., Yu, H., Huang, H., & Pu, B. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5383089 · Cited by 2 · see citing works
Zhang, H., Wang, P., Liu, J., & Qin, J. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108635 · Cited by 1 · see citing works
Wang, Z., Chen, X., Wei, J., Zhang, D., Su, S., & Liu, L. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3583413 · Cited by 1 · see citing works
Srivastava, M., Kumar, G., & Singh, R. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.111022 · Cited by 1 · see citing works
Pavihaa Lakshmi, B., & Sampath, V. (2026) · Scientific Reports · doi:10.1038/s41598-026-36432-2 · Cited by 1 · see citing works
Zhao, G., Wang, Y., Gong, C., Wang, Z., Huang, G., Zhao, X., Chao, T., & Yang, B. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02544-2 · Cited by 1 · see citing works
SAM-FED: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation
Nasirihaghighi, S., Ghamsarian, N., Li, Y., Breeuwer, M. M., Sznitman, R., & Schoeffmann, K. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515518 · Cited by 1 · see citing works
Federated learning for medical image analysis: Methods, challenges, and future directions
Le, T. T., Tran, P.-N., Pham, N. T., Manavalan, B., Shen, L., Hong, C. S., & Dang, D. N. M. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104976 · Cited by 1 · see citing works
Awais, M., & Junejo, R. H. (2026) · Diagnostics · doi:10.3390/diagnostics16162637 · Cited by 1 · see citing works
A Comparative Study on Semi-Supervised Medical Image Segmentation Methods
He, T., Liu, J., & Cao, J. (2024) · International Conference on Cyber-Physical Social Intelligence (ICCSI) · doi:10.1109/iccsi62669.2024.10799465
Real-world implementation and application of federated medical image segmentation
Wang, L., Wang, J., Yang, J. B., & Tan, X. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323641-9.00022-4
Self-citation(2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323641-9.00026-1
卢, 金. (2025) · Modeling and Simulation · doi:10.12677/mos.2025.145414
Hou, J., Yang, C., Zou, Q., Chen, J., & Nie, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9872-1_13
Source Camera Model Identification via Federated Learning Using Laplacian-Based Patches
Chakraborty, R., & Goyal, P. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3629009
He, A., Wu, Y., Shen, L., Li, T., Zou, K., & Fu, H. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.115228
Federated and Split Learning with Frugal Labelling and Label Scarcity in Healthcare Datasets
Sello, P., Bagula, A., & Kahenga, F. (2025) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc65549.2025.11325763
Megala, M., & Dayana, T. R. N. (2025) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca66444.2025.11382863
P., F. R. P., Ali, M. A. S., Khaldi, N. A., Sunder, R., Rahoof, P. P., Lilhore, U. K., Simaiya, S., Ghith, E. S., Hussien, S. A., & Tekeste, L. G. (2026) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-026-09761-6
A semi-supervised deep forest framework based on margin distribution optimization for tabular data
Lyu, S.-H., Xu, J.-L., He, Y.-X., Wang, Y., Ye, B., & Zhang, Q. (2026) · Information Sciences · doi:10.1016/j.ins.2026.123615
Zhai, C., Liu, H., Shang, E., Lu, J., Qiu, Z., & Du, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116371
Junejo, R. H., Awais, M., & Qamar, A. M. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00881-6
Saha, D., Lin, Y., & Ghose, D. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3828166
Federated Semi-Supervised Learning with Progressive Contrast-to-Consistency Learning
Liu, R., Zheng, J., & Chen, S. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582765
FedPKD + : A Prototype-Based Framework for Efficient and Flexible Heterogeneous Federated Learning
Liu, T., Lyu, F., Li, S., Tang, C., Deng, Y., Zhou, C., & Wu, H. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3710770
Qiao, D., Ao, X., Zhang, L., Cao, Y., Le, J., Zhang, D., Zhou, Y., Guo, S., & Chen, X. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3717589
Prototype-aware Channel-Selective Spatial Interaction for Semi-supervised Medical Image SegmentationPreprint
Zhang, J., Jiang, Y., Yao, Y., Zhu, K., Wang, X., & Tian, X. (2026) · Research Square · doi:10.21203/rs.3.rs-10575793/v1
Semwal, R., Aier, I., & Varadwaj, P. K. (2026) · Intelligent Data Analysis · doi:10.1177/1088467x261474319
Federated incremental semantic segmentation in medical images for abdominal and pelvic CT images
Huang, M., Fan, S., Deng, C., Xu, Y., & Zheng, K. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.134148
Adaptive shuffle-boosting federated learning for imbalanced medical image analysis
Li, Y., Liu, Y., Gao, W., Cai, L., Wang, R., & Fan, X. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114743
FSSG: Federated Semi-Supervised Learning With Self-Supervision and Global Coordination
Li, K., Xie, Q., Huang, J., & Luo, B. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3729936
Stomach Polyp Segmentation Using Attention Based Unet 3+ Model
Panigrahi, R., Pradhan, P. P., Senapati, S., Sahoo, S. K., Mallik, L., & Behera, D. P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-33581-4_16
Cheng, Y., Dai, L., Liao, Y., Liu, W., Lv, K., Wang, Y., Chen, H., & Zhou, S. (2026) · Pattern Recognition Letters · doi:10.1016/j.patrec.2026.09.037
Retrieval-Guided Semi-supervised Federated Learning for Medical Image Segmentation
Park, H., Kim, S., Arsalane, W., An, S., & Park, S. H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38069-2_50
Deep semi-supervised learning for medical image analysis: A survey
Shyamalee, T., Nishankar, S., Thuseethan, S., Wimalasooriya, C., & Sebastian, Y. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135371
Suha, S. A., Islam, M. N., & Shahriyar, R. (2026) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-026-00897-y
- Authors
- Ma, Y., Wang, J., Yang, J., & Wang, L.
- Affiliations
- Xiamen University (CN)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2024-05
- Volume
- 43
- Issue
- 5
- Pages
- 1804-1815
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W4390480840
- Cited by
- 55
- References
- 51
- Citations by year
- 2026: 28 · 2025: 23 · 2024: 4
- FWCI
- 7.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Science and Technology of the People’s Republic of China (STI2030-Major Projects2021ZD0201900)
- National Natural Science Foundation of China (62371409)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- semi-supervised federated learning, model heterogeneity, medical image segmentation, annotation cost reduction, pseudo-label generation, skin lesion segmentation, polyp segmentation, privacy-preserving collaborative learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
