mlsanityData to 14 Oct 2026

The Federated Tumor Segmentation (FeTS) Challenge

arXiv 2105.05874 ↗First posted 12 May 2021eess.IV cs.CVcited by 50

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Abstract (arXiv)

This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standard for validation of biomedical image analysis methods. However, the actual performance of participating (even the winning) algorithms on "real-world" clinical data often remains unclear, as the data included in challenges are usually acquired in very controlled settings at few institutions. The seemingly obvious solution of just collecting increasingly more data from more institutions in such challenges does not scale well due to privacy and ownership hurdles. Towards alleviating these concerns, we are proposing the FeTS challenge 2021 to cater towards both the development and the evaluation of models for the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Specifically, the FeTS 2021 challenge uses clinically acquired, multi-institutional magnetic resonance imaging (MRI) scans from the BraTS 2020 challenge, as well as from various remote independent institutions included in the collaborative network of a real-world federation (https://www.fets.ai/). The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i.e. on data from institutional distributions that were not part of the training datasets.

Key facts. Cited by 50 papers in the map (data through 14 Oct 2026). Builds on 23 of its 65 references that are in the map. First posted on arXiv 12 May 2021.

Builds on

23 of 65 references.

References · 23

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11 more
  1. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  2. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 149
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  4. 2020Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domainscited by 124
  5. 2020Test-Time Adaptable Neural Networks for Robust Medical Image Segmentationcited by 102
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Citing papers per year: 2021: 5; 2022: 7; 2023: 7; 2024: 16; 2025: 6; 2026: 9.

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  1. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
  2. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  3. 2021MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluationcited by 88
  4. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  5. 2021QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Resultscited by 28
  6. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  7. 2024FedFMS: Exploring Federated Foundation Models for Medical Image Segmentationcited by 21
  8. 2025BRISC: Annotated Dataset for Brain Tumor Segmentation and Classificationcited by 15
  9. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  10. 2024Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregationcited by 12
  11. 2021FedCostWAvg: A new averaging for better Federated Learningcited by 11
  12. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
38 more
  1. 2024Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imagingcited by 8
  2. 2023FedPIDAvg: A PID controller inspired aggregation method for Federated Learningcited by 7
  3. 2024Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gaincited by 5
  4. 2024Federated brain tumor segmentation: an extensive benchmarkcited by 5
  5. 2022Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentationcited by 4
  6. 2023Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentationcited by 4
  7. 2023FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoringcited by 4
  8. 2025Frequency-Calibrated Membership Inference Attacks on Medical Image Diffusion Modelscited by 3
  9. 2026Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentationcited by 3
  10. 2023Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imagingcited by 2
  11. 2023Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentationcited by 2
  12. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  13. 2024Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentationcited by 2
  14. 2024Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentationcited by 2
  15. 2022Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentationcited by 1
  16. 2022Robust Learning Protocol for Federated Tumor Segmentation Challengecited by 1
  17. 2023Finding the Most Transferable Tasks for Brain Image Segmentationcited by 1
  18. 2023Whole-brain radiomics for clustered federated personalization in brain tumor segmentationcited by 1
  19. 2024FedPID: An Aggregation Method for Federated Learningcited by 1
  20. 2025Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentiallycited by 1
  21. 2025Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentationcited by 1
  22. 2025PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learningcited by 1
  23. 2026OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imagingcited by 1
  24. 2022Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patientscited by 0
  25. 2022FedGraph: an Aggregation Method from Graph Perspectivecited by 0
  26. 2024Non-parametric regularization for class imbalance federated medical image classificationcited by 0
  27. 2024Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based Interpretabilitycited by 0
  28. 2024GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imagingcited by 0
  29. 2024Selecting the Best Sequential Transfer Path for Medical Image Segmentation with Limited Labeled Datacited by 0
  30. 2024A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applicationscited by 0
  31. 2025FedPOD: the deployable units of training for federated learningcited by 0
  32. 2026Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalizationcited by 0
  33. 2026FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentationcited by 0
  34. 2026Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Studycited by 0
  35. 2026MedFL-Stress: A Systematic Robustness Evaluation of Federated Brain Tumor Segmentation under Cross-Hospital MRI Appearance Shiftcited by 0
  36. 2026MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalitiescited by 0
  37. 2026FedASAP: Activation Statistics-driven Structured Adaptive Pruning for Efficient Personalized Federated Learning for Lesion Segmentation on brain MRIcited by 0
  38. 2026Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentationcited by 0