mlsanityData to 14 Oct 2026

One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis

arXiv 2207.06509 ↗First posted 13 Jul 2022eess.IV cs.CV cs.LGcited by 22

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Abstract (arXiv)

Multi-institutional collaborations are key for learning generalizable MRI synthesis models that translate source- onto target-contrast images. To facilitate collaboration, federated learning (FL) adopts decentralized training and mitigates privacy concerns by avoiding sharing of imaging data. However, FL-trained synthesis models can be impaired by the inherent heterogeneity in the data distribution, with domain shifts evident when common or variable translation tasks are prescribed across sites. Here we introduce the first personalized FL method for MRI Synthesis (pFLSynth) to improve reliability against domain shifts. pFLSynth is based on an adversarial model that produces latents specific to individual sites and source-target contrasts, and leverages novel personalization blocks to adaptively tune the statistics and weighting of feature maps across the generator stages given latents. To further promote site specificity, partial model aggregation is employed over downstream layers of the generator while upstream layers are retained locally. As such, pFLSynth enables training of a unified synthesis model that can reliably generalize across multiple sites and translation tasks. Comprehensive experiments on multi-site datasets clearly demonstrate the enhanced performance of pFLSynth against prior federated methods in multi-contrast MRI synthesis.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 55 of its 108 references that are in the map. First posted on arXiv 13 Jul 2022.

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55 of 108 references.

References · 55

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38 more
  1. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
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Citing papers per year: 2023: 6; 2024: 7; 2025: 6; 2026: 3.

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  1. 2023Unified Multi-Modal Image Synthesis for Missing Modality Imputationcited by 40
  2. 2024I2I-Mamba: Multi-modal medical image synthesis via selective state space modelingcited by 26
  3. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  4. 2023Learning Fourier-Constrained Diffusion Bridges for MRI Reconstructioncited by 14
  5. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  6. 2024DenoMamba: A fused state-space model for low-dose CT denoisingcited by 11
  7. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  8. 2024FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesiscited by 6
  9. 2023Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstructioncited by 5
  10. 2025Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstructioncited by 5
  11. 2025Virtual Full-stack Scanning of Brain MRI via Imputing Any Quantised Codecited by 4
  12. 2023FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoisingcited by 3
10 more
  1. 2023Adapting model-based deep learning to multiple acquisition conditions: Ada-MoDLcited by 3
  2. 2025Adaptation of Foundation Models for Medical Image Analysis: Strategies, Challenges, and Future Directionscited by 3
  3. 2025Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Datacited by 2
  4. 2025Federated Learning for Large Models in Medical Imaging: A Comprehensive Reviewcited by 2
  5. 2025A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neckcited by 1
  6. 2024Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learningcited by 0
  7. 2024Machine Learning Techniques for MRI Data Processing at Expanding Scalecited by 0
  8. 2026NeuroSSM: Multiscale Differential State-Space Modeling for Context-Aware fMRI Analysiscited by 0
  9. 2026Towards Personalized Multi-Modal MRI Synthesis across Heterogeneous Datasetscited by 0
  10. 2026FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learningcited by 0