Children’s Nutrition Research Center Staff Publications
Language
English
Publication Date
7-8-2025
Journal
Nature Communications
DOI
10.1038/s41467-025-60466-1
PMID
40628696
PMCID
PMC12238412
PubMedCentral® Posted Date
7-8-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.
Keywords
Humans, Benchmarking, Algorithms, Brain Neoplasms, Image Processing, Computer-Assisted, Artificial Intelligence, Magnetic Resonance Imaging
Published Open-Access
yes
Recommended Citation
Zenk, Maximilian; Baid, Ujjwal; Pati, Sarthak; et al., "Towards Fair Decentralized Benchmarking of Healthcare AI Algorithms With the Federated Tumor Segmentation (FeTS) Challenge" (2025). Children’s Nutrition Research Center Staff Publications. 300.
https://digitalcommons.library.tmc.edu/staff_pub/300
Included in
Biochemical Phenomena, Metabolism, and Nutrition Commons, Dietetics and Clinical Nutrition Commons, Endocrinology, Diabetes, and Metabolism Commons, Nutrition Commons