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| Home > Publications Database > Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge. > print |
| 001 | 279520 | ||
| 005 | 20250720001605.0 | ||
| 024 | 7 | _ | |a 10.1038/s41467-025-60466-1 |2 doi |
| 024 | 7 | _ | |a pmid:40628696 |2 pmid |
| 024 | 7 | _ | |a altmetric:179085903 |2 altmetric |
| 037 | _ | _ | |a DZNE-2025-00843 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 500 |
| 100 | 1 | _ | |a Zenk, Maximilian |0 0000-0002-8933-5995 |b 0 |
| 245 | _ | _ | |a Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge. |
| 260 | _ | _ | |a [London] |c 2025 |b Springer Nature |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1752571792_17353 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 353 - Clinical and Health Care Research (POF4-353) |0 G:(DE-HGF)POF4-353 |c POF4-353 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: pub.dzne.de |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Benchmarking: methods |2 MeSH |
| 650 | _ | 2 | |a Algorithms |2 MeSH |
| 650 | _ | 2 | |a Brain Neoplasms: diagnostic imaging |2 MeSH |
| 650 | _ | 2 | |a Image Processing, Computer-Assisted: methods |2 MeSH |
| 650 | _ | 2 | |a Artificial Intelligence |2 MeSH |
| 650 | _ | 2 | |a Magnetic Resonance Imaging |2 MeSH |
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