mlsanityData to 7 Oct 2026

Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

arXiv 2106.08600 ↗First posted 16 Jun 2021cs.CVcited by 42

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Abstract (arXiv)

Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms only allow the supervised training setting, most hospitals in realistic usually cannot afford the intricate data labeling due to absence of budget or expertise. This paper studies a practical yet challenging FL problem, named textitFederated Semi-supervised Learning (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i.e., hospitals). We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme. The proposed learning scheme explicitly connects the learning across labeled and unlabeled clients by aligning their extracted disease relationships, thereby mitigating the deficiency of task knowledge at unlabeled clients and promoting discriminative information from unlabeled samples. We validate our method on two large-scale medical image classification datasets. The effectiveness of our method has been demonstrated with the clear improvements over state-of-the-arts as well as the thorough ablation analysis on both tasksfootnoteCode will be made available at urlhttps://github.com/liuquande/FedIRM.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 16 Jun 2021.

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24 of 35 references.

References · 24

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Densely Connected Convolutional Networkscited by 6,691
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  4. 2020The Future of Digital Health with Federated Learningcited by 625
  5. 2017Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultscited by 470
  6. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  7. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  8. 2018Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysiscited by 178
  9. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  10. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
  11. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  12. 2020MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Datacited by 121
12 more
  1. 2017Deep Learning for Medical Image Processing: Overview, Challenges and Futurecited by 104
  2. 2020Unpaired Multi-modal Segmentation via Knowledge Distillationcited by 81
  3. 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
  4. 2020Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Modelcited by 59
  5. 2019Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Modelcited by 53
  6. 2020FocalMix: Semi-Supervised Learning for 3D Medical Image Detectioncited by 52
  7. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  8. 2020Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Datacited by 38
  9. 2019GraphXNET- Chest X-Ray Classification Under Extreme Minimal Supervisioncited by 14
  10. 2020Fed-Sim: Federated Simulation for Medical Imagingcited by 14
  11. 2020Semi-supervised Medical Image Classification with Global Latent Mixingcited by 13
  12. 2020Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Modelscited by 10

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Citing papers per year: 2021: 1; 2022: 9; 2023: 13; 2024: 13; 2025: 3; 2026: 3.

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  1. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  2. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  3. 2023FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneitycited by 33
  4. 2022RSCFed: Random Sampling Consensus Federated Semi-supervised Learningcited by 32
  5. 2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 22
  6. 2022IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentationcited by 21
  7. 2022Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phasescited by 18
  8. 2022FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classificationcited by 18
  9. 2023Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directionscited by 14
  10. 2022Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalancecited by 13
  11. 2023Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulatorscited by 9
  12. 2024Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imagingcited by 9
30 more
  1. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  2. 2022Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotationcited by 7
  3. 2023Towards Unbiased Training in Federated Open-world Semi-supervised Learningcited by 6
  4. 2024Robust Training of Federated Models with Extremely Label Deficiencycited by 6
  5. 2024FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentationcited by 6
  6. 2023Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstructioncited by 5
  7. 2024FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneitycited by 5
  8. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  9. 2024Invariant Shape Representation Learning For Image Classificationcited by 5
  10. 2023FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoringcited by 4
  11. 2023Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classificationcited by 4
  12. 2023Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging datacited by 4
  13. 2022Dual Class-Aware Contrastive Federated Semi-Supervised Learningcited by 3
  14. 2023Scale Federated Learning for Label Set Mismatch in Medical Image Classificationcited by 3
  15. 2024Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillationcited by 3
  16. 2024Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistencycited by 3
  17. 2023Federated attention consistent learning models for prostate cancer diagnosis and Gleason gradingcited by 2
  18. 2024A New Perspective to Boost Performance Fairness for Medical Federated Learningcited by 2
  19. 2025FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentationcited by 2
  20. 2025CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environmentscited by 1
  21. 2022Uncertainty Minimization for Personalized Federated Semi-Supervised Learningcited by 0
  22. 2023Federated Semi-Supervised Learning with Annotation Heterogeneitycited by 0
  23. 2023Semi-Supervised Relational Contrastive Learningcited by 0
  24. 2024Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarioscited by 0
  25. 2024Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregationcited by 0
  26. 2024Non-parametric regularization for class imbalance federated medical image classificationcited by 0
  27. 2025Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Surveycited by 0
  28. 2026An Adaptive Differentially Private Federated Learning Frameworkcited by 0
  29. 2026ProxyFL: A Proxy-Guided Framework for Federated Semi-Supervised Learningcited by 0
  30. 2026One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classificationcited by 0