Citations refer to the original publication, not to a Scieee localized version.
Tzortzis, I. N., Gutiérrez Torre, A., Sykiotis, S., Agulló López, F., Bakalos, N., Doulamis, A., Doulamis, N., & Berral García, J. L. (2025). Towards generalizable Federated Learning in medical imaging: a real-world case study on mammography data. Computational and structural biotechnology journal, 28, 106–117. https://doi.org/10.1016/j.csbj.2025.03.031
Tzortzis, Ioannis N., et al. “Towards generalizable Federated Learning in medical imaging: a real-world case study on mammography data.” Computational and structural biotechnology journal, vol. 28, 2025, pp. 106–117. https://doi.org/10.1016/j.csbj.2025.03.031.
Tzortzis, Ioannis N., Alberto Gutiérrez Torre, Stavros Sykiotis, Ferran Agulló López, Nikolaos Bakalos, Anastasios Doulamis, Nikolaos Doulamis, and Josep Lluís Berral García. “Towards generalizable Federated Learning in medical imaging: a real-world case study on mammography data.” Computational and structural biotechnology journal 28 (2025): 106–117. https://doi.org/10.1016/j.csbj.2025.03.031.
Tzortzis, I. N. et al. (2025) ‘Towards generalizable Federated Learning in medical imaging: a real-world case study on mammography data’, Computational and structural biotechnology journal, 28, pp. 106–117. Available at: https://doi.org/10.1016/j.csbj.2025.03.031.
I. N. Tzortzis et al., “Towards generalizable Federated Learning in medical imaging: a real-world case study on mammography data,” Computational and structural biotechnology journal, vol. 28, pp. 106–117, 2025, doi: 10.1016/j.csbj.2025.03.031.
@article{tzortzis2025towards,
author = {Tzortzis, Ioannis N. and Gutiérrez Torre, Alberto and Sykiotis, Stavros and Agulló López, Ferran and Bakalos, Nikolaos and Doulamis, Anastasios and Doulamis, Nikolaos and Berral García, Josep Lluís},
title = {Towards generalizable Federated Learning in medical imaging: a real-world case study on mammography data},
journal = {Computational and structural biotechnology journal},
year = {2025},
volume = {28},
pages = {106--117},
publisher = {Elsevier},
doi = {10.1016/j.csbj.2025.03.031},
url = {https://doi.org/10.1016/j.csbj.2025.03.031}
}
