Security and privacy in machine learning for health systems: strategies and challenges (2023)
- Authors:
- USP affiliated authors: TRAINA JUNIOR, CAETANO - ICMC ; TRAINA, AGMA JUCI MACHADO - ICMC ; AGUIAR, ERIKSON JÚLIO DE - ICMC
- Unidade: ICMC
- DOI: 10.1055/s-0043-1768731
- Subjects: SISTEMAS COMPUTADORIZADOS DE REGISTROS MÉDICOS; APRENDIZADO COMPUTACIONAL; DIAGNÓSTICO POR IMAGEM; DIAGNÓSTICO POR COMPUTADOR; SEGURANÇA DE SOFTWARE; PRIVACIDADE
- Keywords: Adversarial attacks; machine learning; medical images; privacy
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Yearbook of Medical Informatics
- ISSN: 2364-0502
- Volume/Número/Paginação/Ano: v. 32, n. 1, p. 269-281, 2023
- Status:
- Artigo aberto em periódico híbrido (Hybrid Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
AGUIAR, Erikson Júlio de e TRAINA JUNIOR, Caetano e TRAINA, Agma Juci Machado. Security and privacy in machine learning for health systems: strategies and challenges. Yearbook of Medical Informatics, v. 32, n. 1, p. 269-281, 2023Tradução . . Disponível em: https://doi.org/10.1055/s-0043-1768731. Acesso em: 01 abr. 2026. -
APA
Aguiar, E. J. de, Traina Junior, C., & Traina, A. J. M. (2023). Security and privacy in machine learning for health systems: strategies and challenges. Yearbook of Medical Informatics, 32( 1), 269-281. doi:10.1055/s-0043-1768731 -
NLM
Aguiar EJ de, Traina Junior C, Traina AJM. Security and privacy in machine learning for health systems: strategies and challenges [Internet]. Yearbook of Medical Informatics. 2023 ; 32( 1): 269-281.[citado 2026 abr. 01 ] Available from: https://doi.org/10.1055/s-0043-1768731 -
Vancouver
Aguiar EJ de, Traina Junior C, Traina AJM. Security and privacy in machine learning for health systems: strategies and challenges [Internet]. Yearbook of Medical Informatics. 2023 ; 32( 1): 269-281.[citado 2026 abr. 01 ] Available from: https://doi.org/10.1055/s-0043-1768731 - RADAR-MIX: how to uncover adversarial attacks in medical image analysis through explainability
- SentinelAdvMedical: toward adversarial attacks detection on medical image classification via Out-Of-Distribution strategies
- MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data
- Assessing vulnerabilities of deep learning explainability in medical image analysis under adversarial settings
- Data augmentation for medical image segmentation: a comparative analysis of traditional techniques and synthetic data generation
- Upgraded SemIndex prototype supporting intelligent database keyword queries through disambiguation, query as you type, and parallel search algorithms
- Content-based image retrieval using approximate shape of objects
- Distance functions association for content-based image retrieval using multiple comparison criteria
- DBM-tree
- An effective cost model for similarity queries in metric spaces
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