Petrini, Daniel Gustavo Pellacani; Shimizu, Carlos; Roela, Rosimeire Aparecida 
; Valente, Gabriel Vansuita; Folgueira, Maria Aparecida Azevedo Koike ; Hae, Yong Kim
Source: IEEE access. Unidades: FM, EP
Subjects: NEOPLASIAS MAMÁRIAS, MAMOGRAFIA, INTELIGÊNCIA ARTIFICIAL
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ABNT
PETRINI, Daniel Gustavo Pellacani et al. Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network. IEEE access, v. 10, p. 77723-77731, 2022Tradução . . Disponível em: https://doi.org/10.1109/ACCESS.2022.3193250. Acesso em: 16 nov. 2025.APA
Petrini, D. G. P., Shimizu, C., Roela, R. A., Valente, G. V., Folgueira, M. A. A. K., & Hae, Y. K. (2022). Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network. IEEE access, 10, 77723-77731. doi:10.1109/ACCESS.2022.3193250NLM
Petrini DGP, Shimizu C, Roela RA, Valente GV, Folgueira MAAK, Hae YK. Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network [Internet]. IEEE access. 2022 ; 10 77723-77731.[citado 2025 nov. 16 ] Available from: https://doi.org/10.1109/ACCESS.2022.3193250Vancouver
Petrini DGP, Shimizu C, Roela RA, Valente GV, Folgueira MAAK, Hae YK. Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network [Internet]. IEEE access. 2022 ; 10 77723-77731.[citado 2025 nov. 16 ] Available from: https://doi.org/10.1109/ACCESS.2022.3193250
