Benchmarking deep learning architectures for hyperspectral in-vivo brain tumor segmentation.
- DOI
- 10.1016/j.cmpb.2026.109571
- Published
- 2026-07-30
- Container
- Comput Methods Programs Biomed
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1016/j.cmpb.2026.109571,
title = {Benchmarking deep learning architectures for hyperspectral in-vivo brain tumor segmentation.},
author = {Vazquez G and Ragusa D and Torti E and Marenzi E and Juarez E and Groba AM and Leporati F.},
year = {2026},
journal = {Comput Methods Programs Biomed},
doi = {10.1016/j.cmpb.2026.109571},
url = {https://doi.org/10.1016/j.cmpb.2026.109571}
}RIS
TY - JOUR TI - Benchmarking deep learning architectures for hyperspectral in-vivo brain tumor segmentation. AU - Vazquez G AU - Ragusa D AU - Torti E AU - Marenzi E AU - Juarez E AU - Groba AM AU - Leporati F. PY - 2026 JO - Comput Methods Programs Biomed DO - 10.1016/j.cmpb.2026.109571 UR - https://doi.org/10.1016/j.cmpb.2026.109571 ER -
APA
G, V., D, R., E, T., E, M., E, J., AM, G., & F., L. (2026). Benchmarking deep learning architectures for hyperspectral in-vivo brain tumor segmentation.. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2026.109571
Source records
- europe-pmc · retrieved 2026-09-25T10:15:54.050Z