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Filtros : "Financiamento FAPESP" "Bioengineering" "Benvenuto, Giovana Augusta" Limpar

Filtros

Artigo de periodico
Risk factors associated with mortality in hospitalized patients with COVID-19 during the omicron wave in Brazil (2022)
Colnago, Marilaine ; Benvenuto, Giovana Augusta ; Casaca, Wallace Correa de Oliveira ; Negri, Rogério Galante ; Fernandes, Eder Gatti ; Cuminato, José Alberto
Fonte: Bioengineering. Unidade: ICMC
Assuntos: ANÁLISE DE REGRESSÃO E DE CORRELAÇÃO, COVID-19, GOVERNO FEDERAL, VACINAÇÃO, HOSPITALIZAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLNAGO, Marilaine et al. Risk factors associated with mortality in hospitalized patients with COVID-19 during the omicron wave in Brazil. Bioengineering, v. 9, n. 10, p. 1-11, 2022Tradução . . Disponível em: https://doi.org/10.3390/bioengineering9100584. Acesso em: 08 out. 2025.
APA
Colnago, M., Benvenuto, G. A., Casaca, W. C. de O., Negri, R. G., Fernandes, E. G., & Cuminato, J. A. (2022). Risk factors associated with mortality in hospitalized patients with COVID-19 during the omicron wave in Brazil. Bioengineering, 9( 10), 1-11. doi:10.3390/bioengineering9100584
NLM
Colnago M, Benvenuto GA, Casaca WC de O, Negri RG, Fernandes EG, Cuminato JA. Risk factors associated with mortality in hospitalized patients with COVID-19 during the omicron wave in Brazil [Internet]. Bioengineering. 2022 ; 9( 10): 1-11.[citado 2025 out. 08 ] Available from: https://doi.org/10.3390/bioengineering9100584
Vancouver
Colnago M, Benvenuto GA, Casaca WC de O, Negri RG, Fernandes EG, Cuminato JA. Risk factors associated with mortality in hospitalized patients with COVID-19 during the omicron wave in Brazil [Internet]. Bioengineering. 2022 ; 9( 10): 1-11.[citado 2025 out. 08 ] Available from: https://doi.org/10.3390/bioengineering9100584
Artigo de periodico
A fully unsupervised deep learning framework for non-rigid fundus image registration (2022)
Benvenuto, Giovana Augusta ; Colnago, Marilaine ; Dias, Maurício Araujo ; Negri, Rogério Galante ; Silva, Erivaldo Antonio da ; Casaca, Wallace Correa de Oliveira
Fonte: Bioengineering. Unidade: ICMC
Assuntos: VISÃO COMPUTACIONAL, APRENDIZADO COMPUTACIONAL, REDES NEURAIS, FUNDO DE OLHO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BENVENUTO, Giovana Augusta et al. A fully unsupervised deep learning framework for non-rigid fundus image registration. Bioengineering, v. 9, n. 8, p. 1-17, 2022Tradução . . Disponível em: https://doi.org/10.3390/bioengineering9080369. Acesso em: 08 out. 2025.
APA
Benvenuto, G. A., Colnago, M., Dias, M. A., Negri, R. G., Silva, E. A. da, & Casaca, W. C. de O. (2022). A fully unsupervised deep learning framework for non-rigid fundus image registration. Bioengineering, 9( 8), 1-17. doi:10.3390/bioengineering9080369
NLM
Benvenuto GA, Colnago M, Dias MA, Negri RG, Silva EA da, Casaca WC de O. A fully unsupervised deep learning framework for non-rigid fundus image registration [Internet]. Bioengineering. 2022 ; 9( 8): 1-17.[citado 2025 out. 08 ] Available from: https://doi.org/10.3390/bioengineering9080369
Vancouver
Benvenuto GA, Colnago M, Dias MA, Negri RG, Silva EA da, Casaca WC de O. A fully unsupervised deep learning framework for non-rigid fundus image registration [Internet]. Bioengineering. 2022 ; 9( 8): 1-17.[citado 2025 out. 08 ] Available from: https://doi.org/10.3390/bioengineering9080369

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