The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods
Linardos, A., Pati, S., Baid, U. , Edwards, B., Foley, P., Ta, K., Chung, V., Sheller, M., Khan, M. I., Jafaritadi, M., Kontio, E., Khan, S., Mächler, L., Ezhov, I., Shit, S., Paetzold, J. C., Grimberg, G., Nickel, M. A., Naccache, D., Siomos, V., Passerat-Palmbach, J., Tarroni, G. 
ORCID: 0000-0002-0341-6138, Kim, D., Klausmann, L. L., Shah, P., Menze, B., Makris, D. & Bakas, S. (2025).
The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods.
Machine Learning for Biomedical Imaging, 3(December 2025),
pp. 757-774.
doi: 10.59275/j.melba.2025-5242
Abstract
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 scans. Unlike previous FeTS challenges, this iteration exclusively evaluates novel weight aggregation methods for increased robustness and efficiency. Participating methods from six teams are evaluated using a standardized FL setup and a multi-institutional dataset derived from the BraTS glioma benchmark—a dataset consisting of 1,251 training cases, 219 validation cases, and 570 hidden test cases, with segmentations of enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Teams are ranked by a cumulative scoring system that accounts for segmentation performance—measured by Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95)—and communication efficiency assessed through the convergence score. A PID-controller-based approach emerges as the top-performing method, achieving a mean DSC of 0.733, 0.761, and 0.751 for ET, TC, and WT, respectively, with corresponding HD95 values of 33.922mm, 33.623mm, and 32.309mm, while also being the most efficient with a convergence score of 0.764. These results contribute to ongoing advances in FL, building on top-performers from previous iterations of the challenge and surpassing them, highlighting the potential of PID controllers as a powerful mechanism for stabilizing and optimizing weight aggregation in FL. The challenge code is available at https://github.com/FeTS-AI/Challenge
| Publication Type: | Article |
|---|---|
| Additional Information: | ©2025 Akis Linardos et al.. License: CC-BY 4.0 |
| Publisher Keywords: | federated learning, biomedical challenge, segmentation, aggregation, brain tumor, glioma, glioblastoma |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
| Departments: | School of Science & Technology School of Science & Technology > Department of Computer Science |
| SWORD Depositor: |
Available under License Creative Commons Attribution.
Download (836kB) | Preview
Export
Downloads
Downloads per month over past year
Metadata
MetadataActions (login required)
Admin Login