GradCAM as an explicability method to evaluate the performance of deep learning models in classifying pediatric arteriovenous malformations (AVM) in arterial spin labeling sequences (ASL).
- DOI
- 10.1007/s13755-025-00377-z
- Published
- 2025 Dec
- Container
- Health information science and systems
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1007/s13755-025-00377-z,
title = {GradCAM as an explicability method to evaluate the performance of deep learning models in classifying pediatric arteriovenous malformations (AVM) in arterial spin labeling sequences (ASL).},
author = {Romagosa J and Mata C and Benítez R and Valls-Esteve A and Bernaus S and Ibnoulkhatib M and Stephan-Otto C and Munuera J},
year = {2025},
journal = {Health information science and systems},
doi = {10.1007/s13755-025-00377-z},
url = {https://doi.org/10.1007/s13755-025-00377-z}
}RIS
TY - JOUR TI - GradCAM as an explicability method to evaluate the performance of deep learning models in classifying pediatric arteriovenous malformations (AVM) in arterial spin labeling sequences (ASL). AU - Romagosa J AU - Mata C AU - Benítez R AU - Valls-Esteve A AU - Bernaus S AU - Ibnoulkhatib M AU - Stephan-Otto C AU - Munuera J PY - 2025 JO - Health information science and systems DO - 10.1007/s13755-025-00377-z UR - https://doi.org/10.1007/s13755-025-00377-z ER -
APA
J, R., C, M., R, B., A, V., S, B., M, I., C, S., & J, M. (2025). GradCAM as an explicability method to evaluate the performance of deep learning models in classifying pediatric arteriovenous malformations (AVM) in arterial spin labeling sequences (ASL).. Health information science and systems. https://doi.org/10.1007/s13755-025-00377-z
Source records
- pubmed · retrieved 2026-09-26T02:28:27.628Z