Faculty, Staff and Student Publications

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

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