mlsanityData to 7 Oct 2026

Personalizing Federated Medical Image Segmentation via Local Calibration

arXiv 2207.04655 ↗First posted 11 Jul 2022cs.CVcited by 28

Not on the map. Why?

Abstract (arXiv)

Medical image segmentation under federated learning (FL) is a promising direction by allowing multiple clinical sites to collaboratively learn a global model without centralizing datasets. However, using a single model to adapt to various data distributions from different sites is extremely challenging. Personalized FL tackles this issue by only utilizing partial model parameters shared from global server, while keeping the rest to adapt to its own data distribution in the local training of each site. However, most existing methods concentrate on the partial parameter splitting, while do not consider the textitinter-site in-consistencies during the local training, which in fact can facilitate the knowledge communication over sites to benefit the model learning for improving the local accuracy. In this paper, we propose a personalized federated framework with Local Calibration (LC-Fed), to leverage the inter-site in-consistencies in both textitfeature- and prediction- levels to boost the segmentation. Concretely, as each local site has its alternative attention on the various features, we first design the contrastive site embedding coupled with channel selection operation to calibrate the encoded features. Moreover, we propose to exploit the knowledge of prediction-level in-consistency to guide the personalized modeling on the ambiguous regions, e.g., anatomical boundaries. It is achieved by computing a disagreement-aware map to calibrate the prediction. Effectiveness of our method has been verified on three medical image segmentation tasks with different modalities, where our method consistently shows superior performance to the state-of-the-art personalized FL methods. Code is available at https://github.com/jcwang123/FedLC.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 29 of its 41 references that are in the map. First posted on arXiv 11 Jul 2022.

Builds on

29 of 41 references.

References · 29

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2020The Future of Digital Health with Federated Learningcited by 625
  6. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  7. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  8. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  9. 2021Towards Personalized Federated Learningcited by 462
  10. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  11. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  12. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
17 more
  1. 2019REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographscited by 226
  2. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  3. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  4. 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformerscited by 159
  5. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
  6. 2019Federated Evaluation of On-device Personalizationcited by 147
  7. 2020Survey of Personalization Techniques for Federated Learningcited by 144
  8. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  9. 2020MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Datacited by 121
  10. 2020Siloed Federated Learning for Multi-Centric Histopathology Datasetscited by 61
  11. 2021SA-Net: Shuffle Attention for Deep Convolutional Neural Networkscited by 54
  12. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  13. 2021Federated Learning with Domain Generalizationcited by 41
  14. 2019Federated Heavy Hitters Discovery with Differential Privacycited by 32
  15. 2021Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learningcited by 25
  16. 2020Fed-Sim: Federated Simulation for Medical Imagingcited by 14
  17. 2020Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Imagescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 10; 2025: 9; 2026: 5.

28 papers.

Sort:

By area:

  1. 2023Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shiftscited by 26
  2. 2023FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noisecited by 14
  3. 2024MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysiscited by 14
  4. 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysiscited by 8
  5. 2024FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentationcited by 6
  6. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  7. 2023Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregationcited by 4
  8. 2024MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysiscited by 4
  9. 2026Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentationcited by 3
  10. 2024Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learningcited by 2
  11. 2024FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation Completenesscited by 2
  12. 2024Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgerycited by 2
16 more
  1. 2024A New Perspective to Boost Performance Fairness for Medical Federated Learningcited by 2
  2. 2025Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentationcited by 2
  3. 2023Medical Federated Model with Mixture of Personalized and Sharing Componentscited by 1
  4. 2024FovEx: Human-Inspired Explanations for Vision Transformers and Convolutional Neural Networkscited by 1
  5. 2025Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentationcited by 1
  6. 2024Federated Black-Box Adaptation for Semantic Segmentationcited by 0
  7. 2025FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentationcited by 0
  8. 2025Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spacescited by 0
  9. 2025CARE: Confidence-aware Ratio Estimation for Medical Biomarkerscited by 0
  10. 2025Adversarial Versus Federated: An Adversarial Learning based Multi-Modality Cross-Domain Federated Medical Segmentationcited by 0
  11. 2025Privacy-Aware Federated nnU-Net for ECG Page Digitizationcited by 0
  12. 2025Personalized Federated Segmentation with Shared Feature Aggregation and Boundary-Focused Calibrationcited by 0
  13. 2026FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentationcited by 0
  14. 2026Federated Active Learning Under Extreme Non-IID and Global Class Imbalancecited by 0
  15. 2026UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignmentcited by 0
  16. 2026FedASAP: Activation Statistics-driven Structured Adaptive Pruning for Efficient Personalized Federated Learning for Lesion Segmentation on brain MRIcited by 0