Data augmentation for medical image segmentation: a comparative analysis of traditional techniques and synthetic data generation (2025)
- Authors:
- USP affiliated authors: TRAINA JUNIOR, CAETANO - ICMC ; TRAINA, AGMA JUCI MACHADO - ICMC ; UCHIDA, MARIANA AYA SUZUKI - ICMC ; AGUIAR, ERIKSON JÚLIO DE - ICMC
- Unidade: ICMC
- DOI: 10.5753/sbbd.2025.247731
- Subjects: APRENDIZAGEM PROFUNDA; PROCESSAMENTO DE IMAGENS; DIAGNÓSTICO POR IMAGEM; TECNOLOGIAS DA SAÚDE
- Keywords: Data augmentation; Medical Imaging; Diffusion models; Segmentation
- Agências de fomento:
- Language: Inglês
- Objetivos de Desenvolvimento Sustentável (ODS):
03. Saúde e bem-estar
- Imprenta:
- Publisher: SBC
- Publisher place: Porto Alegre
- Date published: 2025
- Source:
- Conference titles: Simpósio Brasileiro de Bancos de Dados - SBBD
- Status:
- Artigo publicado em periódico de acesso aberto (Gold Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
UCHIDA, Mariana Aya Suzuki et al. Data augmentation for medical image segmentation: a comparative analysis of traditional techniques and synthetic data generation. 2025, Anais.. Porto Alegre: SBC, 2025. Disponível em: https://doi.org/10.5753/sbbd.2025.247731. Acesso em: 02 abr. 2026. -
APA
Uchida, M. A. S., Aguiar, E. J. de, Traina Junior, C., & Traina, A. J. M. (2025). Data augmentation for medical image segmentation: a comparative analysis of traditional techniques and synthetic data generation. In Anais. Porto Alegre: SBC. doi:10.5753/sbbd.2025.247731 -
NLM
Uchida MAS, Aguiar EJ de, Traina Junior C, Traina AJM. Data augmentation for medical image segmentation: a comparative analysis of traditional techniques and synthetic data generation [Internet]. Anais. 2025 ;[citado 2026 abr. 02 ] Available from: https://doi.org/10.5753/sbbd.2025.247731 -
Vancouver
Uchida MAS, Aguiar EJ de, Traina Junior C, Traina AJM. Data augmentation for medical image segmentation: a comparative analysis of traditional techniques and synthetic data generation [Internet]. Anais. 2025 ;[citado 2026 abr. 02 ] Available from: https://doi.org/10.5753/sbbd.2025.247731 - RADAR-MIX: how to uncover adversarial attacks in medical image analysis through explainability
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