mlsanityData to 7 Oct 2026

MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning

arXiv 2205.01509 ↗First posted 3 May 2022eess.IV cs.CVcited by 3

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Abstract (arXiv)

Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks, due to variance in lesion characteristics imparted by different scanners and acquisition parameters. In this work, we propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms. Specifically, a learnable weight is assigned to each local node during the aggregation process, based on its segmentation performance. In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training. Comparison experiments on two FL MS segmentation scenarios using public and clinical datasets have demonstrated the effectiveness of the proposed method by outperforming other FL methods significantly. Furthermore, the segmentation performance of FL incorporating our proposed aggregation mechanism can exceed centralised training with all the raw data. The extensive evaluation also indicated the superiority of our method when estimating brain volume differences estimation after lesion inpainting.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 54 references that are in the map. First posted on arXiv 3 May 2022.

Builds on

27 of 54 references.

References · 27

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  4. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  5. 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
  6. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  7. 2017Deep Learning for Medical Image Analysiscited by 399
  8. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  9. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  10. 2016Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learningcited by 209
  11. 2019Domain-Specific Batch Normalization for Unsupervised Domain Adaptationcited by 197
  12. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
15 more
  1. 2020Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentationcited by 134
  2. 2016Unsupervised domain adaptation in brain lesion segmentation with adversarial networkscited by 125
  3. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  4. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  5. 2021Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentationcited by 39
  6. 2018One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networkscited by 20
  7. 2017Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approachcited by 19
  8. 2021Multi-task Federated Learning for Heterogeneous Pancreas Segmentationcited by 19
  9. 2020Unsupervised Domain Adaptation via CycleGAN for White Matter Hyperintensity Segmentation in Multicenter MR Imagescited by 15
  10. 2020Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentationcited by 12
  11. 2021Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applicationscited by 10
  12. 2021Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptationcited by 10
  13. 2016Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origincited by 7
  14. 2021Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweightingcited by 3
  15. 2019Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidencecited by 2

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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 1.

3 papers.

  1. 2023Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentationcited by 4
  2. 2023Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregationcited by 4
  3. 2025UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentationcited by 2