| Authors: |
Zenk, M.; Baid, U.; Pati, S.; Linardos, A.; Edwards, B.; Sheller, M.; Foley, P.; Aristizabal, A.; Zimmerer, D.; Gruzdev, A.; Martin, J.; Shinohara, R. T.; Reinke, A.; Isensee, F.; Parampottupadam, S.; Parekh, K.; Floca, R.; Kassem, H.; Baheti, B.; Thakur, S.; Chung, V.; Kushibar, K.; Lekadir, K.; Jiang, M.; Yin, Y.; Yang, H.; Liu, Q.; Chen, C.; Dou, Q.; Heng, P. A.; Zhang, X.; Zhang, S.; Khan, M. I.; Azeem, M. A.; Jafaritadi, M.; Alhoniemi, E.; Kontio, E.; Khan, S. A.; Mächler, L.; Ezhov, I.; Kofler, F.; Shit, S.; Paetzold, J. C.; Loehr, T.; Wiestler, B.; Peiris, H.; Pawar, K.; Zhong, S.; Chen, Z.; Hayat, M.; Egan, G.; Harandi, M.; Isik Polat, E.; Polat, G.; Kocyigit, A.; Temizel, A.; Tuladhar, A.; Tyagi, L.; Souza, R.; Forkert, N. D.; Mouches, P.; Wilms, M.; Shambhat, V.; Maurya, A.; Danannavar, S. S.; Kalla, R.; Anand, V. K.; Krishnamurthi, G.; Nalawade, S.; Ganesh, C.; Wagner, B.; Reddy, D.; Das, Y.; Yu, F. F.; Fei, B.; Madhuranthakam, A. J.; Maldjian, J.; Singh, G.; Ren, J.; Zhang, W.; An, N.; Hu, Q.; Zhang, Y.; Zhou, Y.; Siomos, V.; Tarroni, G.; Passerrat-Palmbach, J.; Rawat, A.; Zizzo, G.; Kadhe, S. R.; Epperlein, J. P.; Braghin, S.; Wang, Y.; Kanagavelu, R.; Wei, Q.; Yang, Y.; Liu, Y.; Kotowski, K.; Adamski, S.; Machura, B.; Malara, W.; Zarudzki, L.; Nalepa, J.; Shi, Y.; Gao, H.; Avestimehr, S.; Yan, Y.; Akbar, A. S.; Kondrateva, E.; Yang, H.; Li, Z.; Wu, H. Y.; Roth, J.; Saueressig, C.; Milesi, A.; Nguyen, Q. D.; Gruenhagen, N. J.; Huang, T. M.; Ma, J.; Singh, H. S. H.; Pan, N. Y.; Zhang, D.; Zeineldin, R. A.; Futrega, M.; Yuan, Y.; Conte, G. M.; Feng, X.; Pham, Q. D.; Xia, Y.; Jiang, Z.; Luu, H. M.; Dobko, M.; Carré, A.; Tuchinov, B.; Mohy-ud-Din, H.; Alam, S.; Singh, A.; Shah, N.; Wang, W.; Sako, C.; Bilello, M.; Ghodasara, S.; Mohan, S.; Davatzikos, C.; Calabrese, E.; Rudie, J.; Villanueva-Meyer, J.; Cha, S.; Hess, C.; Mongan, J.; Ingalhalikar, M.; Jadhav, M.; Pandey, U.; Saini, J.; Huang, R. Y.; Chang, K.; To, M. S.; Bhardwaj, S.; Chong, C.; Agzarian, M.; Kozubek, M.; Lux, F.; Michálek, J.; Matula, P.; Ker; kovský, M.; Kopr; ivová, T.; Dostál, M.; Vybíhal, V.; Pinho, M. C.; Holcomb, J.; Metz, M.; Jain, R.; Lee, M. D.; Lui, Y. W.; Tiwari, P.; Verma, R.; Bareja, R.; Yadav, I.; Chen, J.; Kumar, N.; Gusev, Y.; Bhuvaneshwar, K.; Sayah, A.; Bencheqroun, C.; Belouali, A.; Madhavan, S.; Colen, R. R.; Kotrotsou, A.; Vollmuth, P.; Brugnara, G.; Preetha, C. J.; Sahm, F.; Bendszus, M.; Wick, W.; Mahajan, A.; Balaña, C.; Capellades, J.; Puig, J.; Choi, Y. S.; Lee, S. K.; Chang, J. H.; Ahn, S. S.; Shaykh, H. F.; Herrera-Trujillo, A.; Trujillo, M.; Escobar, W.; Abello, A.; Bernal, J.; Gómez, J.; LaMontagne, P.; Marcus, D. S.; Milchenko, M.; Nazeri, A.; Landman, B.; Ramadass, K.; Xu, K.; Chotai, S.; Chambless, L. B.; Mistry, A.; Thompson, R. C.; Srinivasan, A.; Bapuraj, J. R.; Rao, A.; Wang, N.; Yoshiaki, O.; Moritani, T.; Turk, S.; Lee, J.; Prabhudesai, S.; Garrett, J.; Larson, M.; Jeraj, R.; Li, H.; Weiss, T.; Weller, M.; Bink, A.; Pouymayou, B.; Sharma, S.; Tseng, T. C.; Adabi, S.; Xavier Falcão, A.; Martins, S. B.; Teixeira, B. C. A.; Sprenger, F.; Menotti, D.; Lucio, D. R.; Niclou, S. P.; Keunen, O.; Hau, A. C.; Pelaez, E.; Franco-Maldonado, H.; Loayza, F.; Quevedo, S.; McKinley, R.; Slotboom, J.; Radojewski, P.; Meier, R.; Wiest, R.; Trenkler, J.; Pichler, J.; Necker, G.; Haunschmidt, A.; Meckel, S.; Guevara, P.; Torche, E.; Mendoza, C.; Vera, F.; Ríos, E.; López, E.; Velastin, S. A.; Choi, J.; Baek, S.; Kim, Y.; Ismael, H.; Allen, B.; Buatti, J. M.; Zampakis, P.; Panagiotopoulos, V.; Tsiganos, P.; Alexiou, S.; Haliassos, I.; Zacharaki, E. I.; Moustakas, K.; Kalogeropoulou, C.; Kardamakis, D. M.; Luo, B.; Poisson, L. M.; Wen, N.; Vallières, M.; Loutfi, M. A. L.; Fortin, D.; Lepage, M.; Morón, F.; Mandel, J.; Shukla, G.; Liem, S.; Alexandre, G. S.; Lombardo, J.; Palmer, J. D.; Flanders, A. E.; Dicker, A. P.; Ogbole, G.; Oyekunle, D.; Odafe-Oyibotha, O.; Osobu, B.; Shu’aibu Hikima, M.; Soneye, M.; Dako, F.; Dorcas, A.; Murcia, D.; Fu, E.; Haas, R.; Thompson, J. A.; Ormond, D. R.; Currie, S.; Fatania, K.; Frood, R.; Simpson, A. L.; Peoples, J. J.; Hu, R.; Cutler, D.; Moraes, F. Y.; Tran, A.; Hamghalam, M.; Boss, M. A.; Gimpel, J.; Kattil Veettil, D.; Schmidt, K.; Cimino, L.; Price, C.; Bialecki, B.; Marella, S.; Apgar, C.; Jakab, A.; Weber, M. A.; Colak, E.; Kleesiek, J.; Freymann, J. B.; Kirby, J. S.; Maier-Hein, L.; Albrecht, J.; Mattson, P.; Karargyris, A.; Shah, P.; Menze, B.; Maier-Hein, K.; Bakas, S. |
| Article Title: |
Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge |
| 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. © The Author(s) 2025. |
| Keywords: |
validation process; nuclear magnetic resonance imaging; brain tumor; brain neoplasms; magnetic resonance imaging; image analysis; diagnostic imaging; algorithms; simulation; health care; algorithm; brain; quantitative analysis; artificial intelligence; benchmarking; tumor; image processing, computer-assisted; segmentation; image processing; image segmentation; procedures; performance assessment; multiparametric magnetic resonance imaging; humans; human; article; deep learning; cloud computing; imaging algorithm; segmentation algorithm; data accuracy; t2 weighted imaging; t1 weighted imaging; fluid-attenuated inversion recovery imaging; federated learning; adaptive weight aggregation; federated tumor segmentation
|
| Journal Title: |
Nature Communications
|
| Volume: |
16 |
| ISSN: |
2041-1723 |
| Publisher: |
Nature Publishing Group
|
| Date Published: |
2025-07-08 |
| Start Page: |
6274 |
| Language: |
English |
| DOI: |
10.1038/s41467-025-60466-1
|
| PUBMED: |
40628696
|
| PROVIDER: |
scopus
|
| PMCID: |
PMC12238412
|
| DOI/URL: |
|
| Notes: |
Source: Scopus |
