mlsanityData to 14 Oct 2026

Federated Contrastive Learning for Volumetric Medical Image Segmentation

arXiv 2204.10983 ↗First posted 23 Apr 2022eess.IV cs.CV cs.LGcited by 33

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Abstract (arXiv)

Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federated learning (FL) can help in this regard by learning a shared model while keeping training data local for privacy. Traditional FL requires fully-labeled data for training, which is inconvenient or sometimes infeasible to obtain due to high labeling cost and the requirement of expertise. Contrastive learning (CL), as a self-supervised learning approach, can effectively learn from unlabeled data to pre-train a neural network encoder, followed by fine-tuning for downstream tasks with limited annotations. However, when adopting CL in FL, the limited data diversity on each client makes federated contrastive learning (FCL) ineffective. In this work, we propose an FCL framework for volumetric medical image segmentation with limited annotations. More specifically, we exchange the features in the FCL pre-training process such that diverse contrastive data are provided to each site for effective local CL while keeping raw data private. Based on the exchanged features, global structural matching further leverages the structural similarity to align local features to the remote ones such that a unified feature space can be learned among different sites. Experiments on a cardiac MRI dataset show the proposed framework substantially improves the segmentation performance compared with state-of-the-art techniques.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). Builds on 25 of its 34 references that are in the map. First posted on arXiv 23 Apr 2022.

Builds on

25 of 34 references.

References · 25

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  5. 2019Advances and Open Problems in Federated Learningcited by 3,046
  6. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  7. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,857
  8. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  9. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,845
  10. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  11. 2020Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hyperspherecited by 1,405
  12. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,380
13 more
  1. 2019Contrastive Multiview Codingcited by 1,301
  2. 2018Federated Learning with Non-IID Datacited by 1,108
  3. 2019Self-Supervised Learning of Pretext-Invariant Representationscited by 808
  4. 2020The Future of Digital Health with Federated Learningcited by 633
  5. 2019Federated Machine Learning: Concept and Applicationscited by 412
  6. 2020Contrastive learning of global and local features for medical image segmentation with limited annotationscited by 309
  7. 2020Threats to Federated Learning: A Surveycited by 178
  8. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 149
  9. 2020Federated Unsupervised Representation Learningcited by 93
  10. 2021Positional Contrastive Learning for Volumetric Medical Image Segmentationcited by 56
  11. 2017Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networkscited by 42
  12. 2021Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrastcited by 21
  13. 2022Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learningcited by 21

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Citing papers per year: 2022: 10; 2023: 12; 2024: 7; 2025: 4.

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  1. 2023Federated Learning for Medical Image Analysis: A Surveycited by 78
  2. 2022Dissecting Self-Supervised Learning Methods for Surgical Computer Visioncited by 48
  3. 2022Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imagingcited by 39
  4. 2023AutoReP: Automatic ReLU Replacement for Fast Private Network Inferencecited by 29
  5. 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inferencecited by 27
  6. 2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 23
  7. 2022Distributed Contrastive Learning for Medical Image Segmentationcited by 16
  8. 2022Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imagingcited by 14
  9. 2022Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalancecited by 13
  10. 2023RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inferencecited by 12
  11. 2023A Review of Predictive and Contrastive Self-supervised Learning for Medical Imagescited by 10
  12. 2023On-Device Unsupervised Image Segmentationcited by 10
21 more
  1. 2023Cross-Modal Vertical Federated Learning for MRI Reconstructioncited by 9
  2. 2022Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Modelcited by 8
  3. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  4. 2022Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotationcited by 7
  5. 2023QuMoS: A Framework for Preserving Security of Quantum Machine Learning Modelcited by 6
  6. 2022Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalizationcited by 4
  7. 2024All-in-One Tuning and Structural Pruning for Domain-Specific LLMscited by 4
  8. 2025FROG: Fair Removal on Graphscited by 4
  9. 2024Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equationcited by 3
  10. 2023A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Imagescited by 2
  11. 2023Edge-InversionNet: Enabling Efficient Inference of InversionNet on Edge Devicescited by 2
  12. 2024Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreementscited by 1
  13. 2025Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Applicationcited by 1
  14. 2025FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learningcited by 1
  15. 2022Joint Embedding of 2D and 3D Networks for Medical Image Anomaly Detectioncited by 0
  16. 2023Semi-Supervised Relational Contrastive Learningcited by 0
  17. 2023Tackling Heterogeneity in Medical Federated learning via Vision Transformerscited by 0
  18. 2024Membership Information Leakage in Federated Contrastive Learningcited by 0
  19. 2024Anatomical 3D Style Transfer Enabling Efficient Federated Learning with Extremely Low Communication Costscited by 0
  20. 2024Federated Black-Box Adaptation for Semantic Segmentationcited by 0
  21. 2025Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Surveycited by 0