| Texto completo | |
| Autor(es): |
Honorato, Eduardo S.
;
Uchida, Mariana Aya S.
;
Traina, Agma J. M.
;
Wolf, Denis F.
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2025 IEEE 38TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS; v. N/A, p. 6-pg., 2025-01-01. |
| Resumo | |
Medical image segmentation plays a vital role in numerous applications and has gained significant attention since the introduction of the U-Net model, which enabled convolutional neural networks to achieve high performance with manageable computational costs. Recently, attention mechanisms have emerged as a promising approach to enhance model performance by emphasizing relevant features while suppressing irrelevant ones. This study explores the integration of channel and spatial attention mechanisms into the U-Net architecture, evaluating their impact on segmentation performance and computational cost. Experiments conducted on six public medical imaging datasets demonstrated performance improvements, with Intersection over Union (IoU) gains ranging from 1.62% to 33.66% compared to the original U-Net. These results highlight the potential of attention mechanisms to significantly improve the efficiency and effectiveness of medical image segmentation models. (AU) | |
| Processo FAPESP: | 24/13328-9 - Gestão inteligente de dados multimodais de saúde para tomada de decisão em cenários de big data: IHealth-MD |
| Beneficiário: | Agma Juci Machado Traina |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 23/18026-8 - Centro de Ciência de Dados para Estatísticas Públicas (CCDEP) |
| Beneficiário: | Carlos Eduardo Torres Freire |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Ciência para o Desenvolvimento |
| Processo FAPESP: | 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD) |
| Beneficiário: | Agma Juci Machado Traina |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |