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@ARTICLE{Zenk:302817,
author = {M. Zenk$^*$ and U. Baid and S. Pati and A. Linardos and B.
Edwards and M. Sheller and P. Foley and A. Aristizabal and
D. Zimmerer$^*$ and A. Gruzdev and J. Martin and R. T.
Shinohara and A. Reinke$^*$ and F. Isensee$^*$ and S.
Parampottupadam$^*$ and K. Parekh$^*$ and R. O. Floca$^*$
and H. Kassem and B. Baheti and S. Thakur and V. Chung and
K. Kushibar and K. Lekadir and M. Jiang and Y. Yin and H.
Yang and Q. Liu and C. Chen and Q. Dou and P.-A. Heng and X.
Zhang and S. Zhang and M. I. Khan and M. A. Azeem and M.
Jafaritadi and E. Alhoniemi and E. Kontio and S. A. Khan and
L. Mächler and I. Ezhov and F. Kofler and S. Shit and J. C.
Paetzold and T. Loehr and B. Wiestler and H. Peiris and K.
Pawar and S. Zhong and Z. Chen and M. Hayat and G. Egan and
M. Harandi and E. Isik Polat and G. Polat and A. Kocyigit
and A. Temizel and A. Tuladhar and L. Tyagi and R. Souza and
N. D. Forkert and P. Mouches and M. Wilms and V. Shambhat
and A. Maurya and S. S. Danannavar and R. Kalla and V. K.
Anand and G. Krishnamurthi and S. Nalawade and C. Ganesh and
B. Wagner and D. Reddy and Y. Das and F. F. Yu and B. Fei
and A. J. Madhuranthakam and J. Maldjian and G. Singh and J.
Ren and W. Zhang and N. An and Q. Hu and Y. Zhang and Y.
Zhou and V. Siomos and G. Tarroni and J. Passerrat-Palmbach
and A. Rawat and G. Zizzo and S. R. Kadhe and J. P.
Epperlein and S. Braghin and Y. Wang and R. Kanagavelu and
Q. Wei and Y. Yang and Y. Liu and K. Kotowski and S. Adamski
and B. Machura and W. Malara and L. Zarudzki and J. Nalepa
and Y. Shi and H. Gao and S. Avestimehr and Y. Yan and A. S.
Akbar and E. Kondrateva and H. Yang and Z. Li and H.-Y. Wu
and J. Roth and C. Saueressig and A. Milesi and Q. D. Nguyen
and N. J. Gruenhagen and T.-M. Huang and J. Ma and H. S. H.
Singh and N.-Y. Pan and D. Zhang and R. A. Zeineldin and M.
Futrega and Y. Yuan and G. M. Conte and X. Feng and Q. D.
Pham and Y. Xia and Z. Jiang and H. M. Luu and M. Dobko and
A. Carré and B. Tuchinov and H. Mohy-Ud-Din and S. Alam and
A. Singh and N. Shah and W. Wang and C. Sako and M. Bilello
and S. Ghodasara and S. Mohan and C. Davatzikos and E.
Calabrese and J. Rudie and J. Villanueva-Meyer and S. Cha
and C. Hess and J. Mongan and M. Ingalhalikar and M. Jadhav
and U. Pandey and J. Saini and R. Y. Huang and K. Chang and
M.-S. To and S. Bhardwaj and C. Chong and M. Agzarian and M.
Kozubek and F. Lux and J. Michálek and P. Matula and M. Ker
Kovský and T. Kopr Ivová and M. Dostál and V. Vybíhal
and M. C. Pinho and J. Holcomb and M. Metz and R. Jain and
M. D. Lee and Y. W. Lui and P. Tiwari and R. Verma and R.
Bareja and I. Yadav and J. Chen and N. Kumar and Y. Gusev
and K. Bhuvaneshwar and A. Sayah and C. Bencheqroun and A.
Belouali and S. Madhavan and R. R. Colen and A. Kotrotsou
and P. Vollmuth$^*$ and G. Brugnara and C. J. Preetha and F.
Sahm$^*$ and M. Bendszus and W. Wick and A. Mahajan and C.
Balaña and J. Capellades and J. Puig and Y. S. Choi and
S.-K. Lee and J. H. Chang and S. S. Ahn and H. F. Shaykh and
A. Herrera-Trujillo and M. Trujillo and W. Escobar and A.
Abello and J. Bernal and J. Gómez and P. LaMontagne and D.
S. Marcus and M. Milchenko and A. Nazeri and B. Landman and
K. Ramadass and K. Xu and S. Chotai and L. B. Chambless and
A. Mistry and R. C. Thompson and A. Srinivasan and J. R.
Bapuraj and A. Rao and N. Wang and O. Yoshiaki and T.
Moritani and S. Turk and J. Lee and S. Prabhudesai and J.
Garrett and M. Larson and R. Jeraj and H. Li and T. Weiss
and M. Weller and A. Bink and B. Pouymayou and S. Sharma and
T.-C. Tseng and S. Adabi and A. Xavier Falcão and S. B.
Martins and B. C. A. Teixeira and F. Sprenger and D. Menotti
and D. R. Lucio and S. P. Niclou and O. Keunen and A.-C. Hau
and E. Pelaez and H. Franco-Maldonado and F. Loayza and S.
Quevedo and R. McKinley and J. Slotboom and P. Radojewski
and R. Meier and R. Wiest and J. Trenkler and J. Pichler and
G. Necker and A. Haunschmidt and S. Meckel and P. Guevara
and E. Torche and C. Mendoza and F. Vera and E. Ríos and E.
López and S. A. Velastin and J. Choi and S. Baek and Y. Kim
and H. Ismael and B. Allen and J. M. Buatti and P. Zampakis
and V. Panagiotopoulos and P. Tsiganos and S. Alexiou and I.
Haliassos and E. I. Zacharaki and K. Moustakas and C.
Kalogeropoulou and D. M. Kardamakis and B. Luo and L. M.
Poisson and N. Wen and M. Vallières and M. A. L. Loutfi and
D. Fortin and M. Lepage and F. Morón and J. Mandel and G.
Shukla and S. Liem and G. S. Alexandre and J. Lombardo and
J. D. Palmer and A. E. Flanders and A. P. Dicker and G.
Ogbole and D. Oyekunle and O. Odafe-Oyibotha and B. Osobu
and M. Shu'aibu Hikima and M. Soneye and F. Dako and A.
Dorcas and D. Murcia and E. Fu and R. Haas and J. A.
Thompson and D. R. Ormond and S. Currie and K. Fatania and
R. Frood and A. L. Simpson and J. J. Peoples and R. Hu and
D. Cutler and F. Y. Moraes and A. Tran and M. Hamghalam and
M. A. Boss and J. Gimpel and D. Kattil Veettil and K.
Schmidt and L. Cimino and C. Price and B. Bialecki and S.
Marella and C. Apgar and A. Jakab and M.-A. Weber and E.
Colak and J. Kleesiek and J. B. Freymann and J. S. Kirby and
L. Maier-Hein$^*$ and J. Albrecht and P. Mattson and A.
Karargyris and P. Shah and B. Menze and K. Maier-Hein$^*$
and S. Bakas},
title = {{T}owards fair decentralized benchmarking of healthcare
{AI} algorithms with the {F}ederated {T}umor {S}egmentation
({F}e{TS}) challenge.},
journal = {Nature Communications},
volume = {16},
number = {1},
issn = {2041-1723},
address = {[London]},
publisher = {Springer Nature},
reportid = {DKFZ-2025-01357},
pages = {6274},
year = {2025},
note = {#EA:E230#LA:E230#},
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: methods / Algorithms / Brain
Neoplasms: diagnostic imaging / Image Processing,
Computer-Assisted: methods / Artificial Intelligence /
Magnetic Resonance Imaging},
cin = {E230 / E130 / B300 / HD01},
ddc = {500},
cid = {I:(DE-He78)E230-20160331 / I:(DE-He78)E130-20160331 /
I:(DE-He78)B300-20160331 / I:(DE-He78)HD01-20160331},
pnm = {315 - Bildgebung und Radioonkologie (POF4-315)},
pid = {G:(DE-HGF)POF4-315},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:40628696},
pmc = {pmc:PMC12238412},
doi = {10.1038/s41467-025-60466-1},
url = {https://inrepo02.dkfz.de/record/302817},
}
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