mlsanityData to 7 Oct 2026

Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

arXiv 2103.02148 ↗First posted 3 Mar 2021eess.IV cs.CVcited by 78

Not on the map. Why?

Abstract (arXiv)

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction. However, these methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. In order to overcome this challenge, we propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy. However, the generalizability of models trained with the FL setting can still be suboptimal due to domain shift, which results from the data collected at multiple institutions with different sensors, disease types, and acquisition protocols, etc. With the motivation of circumventing this challenge, we propose a cross-site modeling for MR image reconstruction in which the learned intermediate latent features among different source sites are aligned with the distribution of the latent features at the target site. Extensive experiments are conducted to provide various insights about FL for MR image reconstruction. Experimental results demonstrate that the proposed framework is a promising direction to utilize multi-institutional data without compromising patients' privacy for achieving improved MR image reconstruction. Our code will be available at https://github.com/guopengf/FLMRCM.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). Builds on 22 of its 41 references that are in the map. First posted on arXiv 3 Mar 2021.

Builds on

22 of 41 references.

References · 22

  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. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,323
  5. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,079
  6. 2019Agnostic Federated Learningcited by 451
  7. 2019Federated Machine Learning: Concept and Applicationscited by 409
  8. 2015Simultaneous Deep Transfer Across Domains and Taskscited by 205
  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. 2018An overview of deep learning in medical imaging focusing on MRIcited by 140
  12. 2019Federated Adversarial Domain Adaptationcited by 140
10 more
  1. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  2. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  3. 2019Deep Learning Methods for Parallel Magnetic Resonance Image Reconstructioncited by 81
  4. 2019Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learningcited by 60
  5. 2018Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networkscited by 53
  6. 2018DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Trainingcited by 26
  7. 2017Deep Generative Adversarial Networks for Compressed Sensing Automates MRIcited by 15
  8. 2019Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networkscited by 13
  9. 2020Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GANcited by 6
  10. 2020Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomascited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 22; 2023: 22; 2024: 9; 2025: 12; 2026: 4.

78 papers.

Sort:

By area:

  1. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  2. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  3. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  4. 2022Improving Generalization in Federated Learning by Seeking Flat Minimacited by 98
  5. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  6. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model samplingcited by 59
  7. 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 50
  8. 2022Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentationcited by 41
  9. 2021Optimality and Stability in Federated Learning: A Game-theoretic Approachcited by 40
  10. 2023Learning Federated Visual Prompt in Null Space for MRI Reconstructioncited by 40
  11. 2023Self-Supervised MRI Reconstruction with Unrolled Diffusion Modelscited by 37
  12. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
38 more
  1. 2022CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learningcited by 34
  2. 2022Federated Learning of Generative Image Priors for MRI Reconstructioncited by 33
  3. 2021SketchEdit: Mask-Free Local Image Manipulation with Partial Sketchescited by 32
  4. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  5. 2021Over-and-Under Complete Convolutional RNN for MRI Reconstructioncited by 29
  6. 2024Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimizationcited by 28
  7. 2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 22
  8. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  9. 2023Federated Learning for Computer Visioncited by 22
  10. 2021Context-Aware Online Client Selection for Hierarchical Federated Learningcited by 20
  11. 2022Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentationcited by 16
  12. 2022FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregationcited by 16
  13. 2022Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imagingcited by 14
  14. 2025Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Modelcited by 13
  15. 2022FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labelscited by 12
  16. 2021SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learningcited by 11
  17. 2023Cross-Modal Vertical Federated Learning for MRI Reconstructioncited by 9
  18. 2022FedVLN: Privacy-preserving Federated Vision-and-Language Navigationcited by 8
  19. 2022Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicinecited by 8
  20. 2023DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image Synthesiscited by 8
  21. 2023Handling Data Heterogeneity via Architectural Design for Federated Visual Recognitioncited by 8
  22. 2022SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalizationcited by 7
  23. 2024Physics-Driven Autoregressive State Space Models for Medical Image Reconstructioncited by 7
  24. 2022Federated Learning with Privacy-Preserving Ensemble Attention Distillationcited by 6
  25. 2022Addressing Heterogeneity in Federated Learning via Distributional Transformationcited by 6
  26. 2023Federated Learning via Input-Output Collaborative Distillationcited by 6
  27. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  28. 2022Undersampled MRI Reconstruction with Side Information-Guided Normalisationcited by 5
  29. 2023Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstructioncited by 5
  30. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  31. 2023FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoringcited by 4
  32. 2023A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentationcited by 4
  33. 2023Federated Learning on Non-iid Data via Local and Global Distillationcited by 4
  34. 2023PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learningcited by 4
  35. 2024Federated Learning under Partially Class-Disjoint Data via Manifold Reshapingcited by 4
  36. 2025Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstructioncited by 4
  37. 2021An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future workscited by 3
  38. 2022Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learningcited by 3

50 most cited of 78 All 78 (JSON)