Nnadozie, E.C.; Merino-Caviedes, S.; de Luis-Román, D.A.; MartÃn-Fernández, M.; Alberola-López, C.
Towards Improved Clinical Adoption of AI Segmentation Models: Benchmarking High-Performance Models for Resource-Constrained Settings. Big Data Cogn. Comput. 2026, 10, 142.
https://doi.org/10.3390/bdcc10050142
AMA Style
Nnadozie EC, Merino-Caviedes S, de Luis-Román DA, MartÃn-Fernández M, Alberola-López C.
Towards Improved Clinical Adoption of AI Segmentation Models: Benchmarking High-Performance Models for Resource-Constrained Settings. Big Data and Cognitive Computing. 2026; 10(5):142.
https://doi.org/10.3390/bdcc10050142
Chicago/Turabian Style
Nnadozie, Emmanuel Chibuikem, Susana Merino-Caviedes, Daniel A. de Luis-Román, Marcos MartÃn-Fernández, and Carlos Alberola-López.
2026. "Towards Improved Clinical Adoption of AI Segmentation Models: Benchmarking High-Performance Models for Resource-Constrained Settings" Big Data and Cognitive Computing 10, no. 5: 142.
https://doi.org/10.3390/bdcc10050142
APA Style
Nnadozie, E. C., Merino-Caviedes, S., de Luis-Román, D. A., MartÃn-Fernández, M., & Alberola-López, C.
(2026). Towards Improved Clinical Adoption of AI Segmentation Models: Benchmarking High-Performance Models for Resource-Constrained Settings. Big Data and Cognitive Computing, 10(5), 142.
https://doi.org/10.3390/bdcc10050142