The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning
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| Formato: | Preprint |
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2025
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| author | Linardos, Akis Pati, Sarthak Baid, Ujjwal Edwards, Brandon Foley, Patrick Ta, Kevin Chung, Verena Sheller, Micah Khan, Muhammad Irfan Jafaritadi, Mojtaba Kontio, Elina Khan, Suleiman Mächler, Leon Ezhov, Ivan Shit, Suprosanna Paetzold, Johannes C. Grimberg, Gustav Nickel, Manuel A. Naccache, David Siomos, Vasilis Passerat-Palmbach, Jonathan Tarroni, Giacomo Kim, Daewoon Klausmann, Leonard L. Shah, Prashant Menze, Bjoern Makris, Dimitrios Bakas, Spyridon |
| author_facet | Linardos, Akis Pati, Sarthak Baid, Ujjwal Edwards, Brandon Foley, Patrick Ta, Kevin Chung, Verena Sheller, Micah Khan, Muhammad Irfan Jafaritadi, Mojtaba Kontio, Elina Khan, Suleiman Mächler, Leon Ezhov, Ivan Shit, Suprosanna Paetzold, Johannes C. Grimberg, Gustav Nickel, Manuel A. Naccache, David Siomos, Vasilis Passerat-Palmbach, Jonathan Tarroni, Giacomo Kim, Daewoon Klausmann, Leonard L. Shah, Prashant Menze, Bjoern Makris, Dimitrios Bakas, Spyridon |
| contents | We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in multi-parametric MRI and evaluates new weight aggregation methods aimed at improving robustness and efficiency. Six participating teams were evaluated using a standardized FL setup and a multi-institutional dataset derived from the BraTS glioma benchmark, consisting of 1,251 training cases, 219 validation cases, and 570 hidden test cases with segmentations for enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Teams were ranked using a cumulative scoring system that considered both segmentation performance, measured by Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95), and communication efficiency assessed through the convergence score. A PID-controller-based method achieved the top overall ranking, obtaining mean DSC values of 0.733, 0.761, and 0.751 for ET, TC, and WT, respectively, with corresponding HD95 values of 33.922 mm, 33.623 mm, and 32.309 mm, while also demonstrating the highest communication efficiency with a convergence score of 0.764. These findings advance the state of federated learning for medical imaging, surpassing top-performing methods from previous challenge iterations and highlighting PID controllers as effective mechanisms for stabilizing and optimizing weight aggregation in FL. The challenge code is available at https://github.com/FeTS-AI/Challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning Linardos, Akis Pati, Sarthak Baid, Ujjwal Edwards, Brandon Foley, Patrick Ta, Kevin Chung, Verena Sheller, Micah Khan, Muhammad Irfan Jafaritadi, Mojtaba Kontio, Elina Khan, Suleiman Mächler, Leon Ezhov, Ivan Shit, Suprosanna Paetzold, Johannes C. Grimberg, Gustav Nickel, Manuel A. Naccache, David Siomos, Vasilis Passerat-Palmbach, Jonathan Tarroni, Giacomo Kim, Daewoon Klausmann, Leonard L. Shah, Prashant Menze, Bjoern Makris, Dimitrios Bakas, Spyridon Computer Vision and Pattern Recognition Machine Learning We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in multi-parametric MRI and evaluates new weight aggregation methods aimed at improving robustness and efficiency. Six participating teams were evaluated using a standardized FL setup and a multi-institutional dataset derived from the BraTS glioma benchmark, consisting of 1,251 training cases, 219 validation cases, and 570 hidden test cases with segmentations for enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Teams were ranked using a cumulative scoring system that considered both segmentation performance, measured by Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95), and communication efficiency assessed through the convergence score. A PID-controller-based method achieved the top overall ranking, obtaining mean DSC values of 0.733, 0.761, and 0.751 for ET, TC, and WT, respectively, with corresponding HD95 values of 33.922 mm, 33.623 mm, and 32.309 mm, while also demonstrating the highest communication efficiency with a convergence score of 0.764. These findings advance the state of federated learning for medical imaging, surpassing top-performing methods from previous challenge iterations and highlighting PID controllers as effective mechanisms for stabilizing and optimizing weight aggregation in FL. The challenge code is available at https://github.com/FeTS-AI/Challenge. |
| title | The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2512.06206 |