FSCL-BC: Federated supervised contrastive learning for breast cancer diagnosis with high sensitivity.
- DOI
- 10.1016/j.cmpb.2026.109500
- Published
- 2026 Oct
- Container
- Computer methods and programs in biomedicine
- Publisher
- Not recorded
- Open access
- no
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limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1016/j.cmpb.2026.109500,
title = {FSCL-BC: Federated supervised contrastive learning for breast cancer diagnosis with high sensitivity.},
author = {Ahmed F and Sánchez D and Haddi Z and Domingo-Ferrer J},
year = {2026},
journal = {Computer methods and programs in biomedicine},
doi = {10.1016/j.cmpb.2026.109500},
url = {https://doi.org/10.1016/j.cmpb.2026.109500}
}RIS
TY - JOUR TI - FSCL-BC: Federated supervised contrastive learning for breast cancer diagnosis with high sensitivity. AU - Ahmed F AU - Sánchez D AU - Haddi Z AU - Domingo-Ferrer J PY - 2026 JO - Computer methods and programs in biomedicine DO - 10.1016/j.cmpb.2026.109500 UR - https://doi.org/10.1016/j.cmpb.2026.109500 ER -
APA
F, A., D, S., Z, H., & J, D. (2026). FSCL-BC: Federated supervised contrastive learning for breast cancer diagnosis with high sensitivity.. Computer methods and programs in biomedicine. https://doi.org/10.1016/j.cmpb.2026.109500
Source records
- pubmed · retrieved 2026-09-25T09:00:50.242Z
- europe-pmc · retrieved 2026-09-25T09:00:50.239Z