# The Federated Tumor Segmentation (FeTS) Challenge

arXiv: https://arxiv.org/abs/2105.05874 · HTML: https://mlsanity.com/paper/2105.05874/

## 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 (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [The Importance of Skip Connections in Biomedical Image Segmentation](https://mlsanity.com/paper/1608.04117/) (2016)
- [Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://mlsanity.com/paper/2007.02035/) (2020)
- [Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation](https://mlsanity.com/paper/2004.04668/) (2020)
- [Federated Learning for Breast Density Classification: A Real-World Implementation](https://mlsanity.com/paper/2009.01871/) (2020)
- [BIAS: Transparent reporting of biomedical image analysis challenges](https://mlsanity.com/paper/1910.04071/) (2019)
- [Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables](https://mlsanity.com/paper/1811.03695/) (2018)
- [Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI](https://mlsanity.com/paper/2011.07592/) (2020)
- [GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging](https://mlsanity.com/paper/2103.01006/) (2021)
- [Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning](https://mlsanity.com/paper/2009.13148/) (2020)

## Built on by (most cited first, 30 of 50)

- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation](https://mlsanity.com/paper/2110.01406/) (2021)
- [OpenFL: An open-source framework for Federated Learning](https://mlsanity.com/paper/2105.06413/) (2021)
- [QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results](https://mlsanity.com/paper/2112.10074/) (2021)
- [One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis](https://mlsanity.com/paper/2207.06509/) (2022)
- [FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation](https://mlsanity.com/paper/2403.05408/) (2024)
- [BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification](https://mlsanity.com/paper/2506.14318/) (2025)
- [The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape](https://mlsanity.com/paper/2405.03636/) (2024)
- [Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregation](https://mlsanity.com/paper/2406.03323/) (2024)
- [FedCostWAvg: A new averaging for better Federated Learning](https://mlsanity.com/paper/2111.08649/) (2021)
- [Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation](https://mlsanity.com/paper/2406.12815/) (2024)
- [Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging](https://mlsanity.com/paper/2407.07557/) (2024)
- [FedPIDAvg: A PID controller inspired aggregation method for Federated Learning](https://mlsanity.com/paper/2304.12117/) (2023)
- [Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain](https://mlsanity.com/paper/2405.09409/) (2024)
- [Federated brain tumor segmentation: an extensive benchmark](https://mlsanity.com/paper/2410.17265/) (2024)
- [Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation](https://mlsanity.com/paper/2209.07704/) (2022)
- [Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation](https://mlsanity.com/paper/2301.12617/) (2023)
- [FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring](https://mlsanity.com/paper/2305.00738/) (2023)
- [Frequency-Calibrated Membership Inference Attacks on Medical Image Diffusion Models](https://mlsanity.com/paper/2506.14919/) (2025)
- [Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentation](https://mlsanity.com/paper/2603.04887/) (2026)
- [Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging](https://mlsanity.com/paper/2304.09327/) (2023)
- [Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation](https://mlsanity.com/paper/2310.15371/) (2023)
- [Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review](https://mlsanity.com/paper/2409.16340/) (2024)
- [Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation](https://mlsanity.com/paper/2412.20250/) (2024)
- [Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation](https://mlsanity.com/paper/2412.20253/) (2024)
- [Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation](https://mlsanity.com/paper/2202.08261/) (2022)
- [Robust Learning Protocol for Federated Tumor Segmentation Challenge](https://mlsanity.com/paper/2212.08290/) (2022)
- [Finding the Most Transferable Tasks for Brain Image Segmentation](https://mlsanity.com/paper/2301.00934/) (2023)
- [Whole-brain radiomics for clustered federated personalization in brain tumor segmentation](https://mlsanity.com/paper/2310.11480/) (2023)

## 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.