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Detalhes Referência

Tipo
Artigos em Revista

Tipo de Documento
Artigo Completo

Título
You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction

Participantes na publicação
João Mendes (Author)
Dep. Física
Unidade de I&D e Inovação
LASIGE
Bernardo Oliveira (Author)
Carolina Araújo (Author)
Joana Galrão (Author)
Nuno C. Garcia (Author)
Dep. Informática
LASIGE
Nuno Matela (Author)
Dep. Física
IBEB

Data de Publicação
2025-03

Suporte
Computers in Biology and Medicine

Identificadores da Publicação
ISSN - 0010-4825

Editora
Elsevier BV

Volume
187

Página Inicial
109733

Identificadores do Documento
DOI - https://doi.org/10.1016/j.compbiomed.2025.109733
URL - https://doi.org/10.1016/j.compbiomed.2025.109733

Identificadores de Qualidade
Web Of Science Q1 (2023) - 7.0 - ENGINEERING, BIOMEDICAL
SCOPUS Q1 (2023) - 11.7 - Health Informatics
SCOPUS Q1 (2023) - 11.7 - Computer Science Applications
Web Of Science Q1 (2023) - 7.0 - COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
SCIMAGO Q1 (2023) - 1.481 - Health Informatics
SCIMAGO Q1 (2023) - 1.481 - Computer Science Applications


Exportar referência

APA
João Mendes, Bernardo Oliveira, Carolina Araújo, Joana Galrão, Nuno C. Garcia, Nuno Matela, (2025). You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction. Computers in Biology and Medicine, 187, ISSN 0010-4825. eISSN . https://doi.org/10.1016/j.compbiomed.2025.109733

IEEE
João Mendes, Bernardo Oliveira, Carolina Araújo, Joana Galrão, Nuno C. Garcia, Nuno Matela, "You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction" in Computers in Biology and Medicine, vol. 187, 2025. 10.1016/j.compbiomed.2025.109733

BIBTEX
@article{63640, author = {João Mendes and Bernardo Oliveira and Carolina Araújo and Joana Galrão and Nuno C. Garcia and Nuno Matela}, title = {You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction}, journal = {Computers in Biology and Medicine}, year = 2025, volume = 187 }