mlsanityData to 14 Oct 2026

Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities

arXiv 2406.11636 ↗First posted 17 Jun 2024eess.IV cs.CV cs.LGcited by 5

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Abstract (arXiv)

Segmentation models for brain lesions in MRI are typically developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment the disease using data with a different set of MRI modalities, nor can they segment other types of diseases. Moreover, this training paradigm prevents a model from using the advantages of learning from heterogeneous databases that may contain scans and segmentation labels for different brain pathologies and diverse sets of MRI modalities. Additionally, the confidentiality of patient data often prevents central data aggregation, necessitating a decentralized approach. Is it feasible to use Federated Learning (FL) to train a single model on client databases that contain scans and labels of different brain pathologies and diverse sets of MRI modalities? We demonstrate promising results by combining appropriate, simple, and practical modifications to the model and training strategy: Designing a model with input channels that cover the whole set of modalities available across clients, training with random modality drop, and exploring the effects of feature normalization methods. Evaluation on 7 brain MRI databases with 5 different diseases shows that this FL framework can train a single model achieving very promising results in segmenting all disease types seen during training. Importantly, it can segment these diseases in new databases that contain sets of modalities different from those in training clients. These results demonstrate, for the first time, the feasibility and effectiveness of using FL to train a single 3D segmentation model on decentralised data with diverse brain diseases and MRI modalities, a necessary step towards leveraging heterogeneous real-world databases. Code: https://github.com/FelixWag/FedUniBrain

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 42 of its 59 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

42 of 59 references.

References · 42

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30 more
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Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learningcited by 6
  2. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  3. 2025Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstructioncited by 5
  4. 2024F3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristicscited by 4
  5. 2025Auto-nnU-Net: Towards Automated Medical Image Segmentationcited by 3