Improving U-Net with attention mechanism for medical image segmentation applications (2025)
- Authors:
- USP affiliated authors: TRAINA, AGMA JUCI MACHADO - ICMC ; WOLF, DENIS FERNANDO - ICMC ; HONORATO, EDUARDO SPERLE - ICMC ; UCHIDA, MARIANA AYA SUZUKI - ICMC
- Unidade: ICMC
- DOI: 10.1109/CBMS65348.2025.00060
- Subjects: VISÃO COMPUTACIONAL; PROCESSAMENTO DE IMAGENS; REDES NEURAIS; DIAGNÓSTICO POR IMAGEM
- Keywords: Attention Mechanism; Medical Images Segmentation; U-Net; Computer Vision
- Agências de fomento:
- Language: Inglês
- Sustainable Development Goals (GDS):
03. Good health and well-being
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2025
- Source:
- Título: Proceedings
- ISSN: 2372-9198
- Conference titles: IEEE International Symposium on Computer-Based Medical Systems - CBMS
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
HONORATO, Eduardo Sperle et al. Improving U-Net with attention mechanism for medical image segmentation applications. 2025, Anais.. Los Alamitos: IEEE, 2025. Disponível em: https://doi.org/10.1109/CBMS65348.2025.00060. Acesso em: 25 dez. 2025. -
APA
Honorato, E. S., Uchida, M. A. S., Traina, A. J. M., & Wolf, D. F. (2025). Improving U-Net with attention mechanism for medical image segmentation applications. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS65348.2025.00060 -
NLM
Honorato ES, Uchida MAS, Traina AJM, Wolf DF. Improving U-Net with attention mechanism for medical image segmentation applications [Internet]. Proceedings. 2025 ;[citado 2025 dez. 25 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00060 -
Vancouver
Honorato ES, Uchida MAS, Traina AJM, Wolf DF. Improving U-Net with attention mechanism for medical image segmentation applications [Internet]. Proceedings. 2025 ;[citado 2025 dez. 25 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00060 - Out-of-distribution object detection in autonomous vehicles with Yolo model
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Informações sobre o DOI: 10.1109/CBMS65348.2025.00060 (Fonte: oaDOI API)
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