Assessing vulnerabilities of deep learning explainability in medical image analysis under adversarial settings (2023)
- Authors:
- USP affiliated authors: TRAINA JUNIOR, CAETANO - ICMC ; TRAINA, AGMA JUCI MACHADO - ICMC ; AGUIAR, ERIKSON JÚLIO DE - ICMC ; COSTA, MÁRCUS VINÍCIUS LOBO - ICMC
- Unidade: ICMC
- DOI: 10.1109/CBMS58004.2023.00184
- Subjects: APRENDIZADO COMPUTACIONAL; PROCESSAMENTO DE IMAGENS; RECONHECIMENTO DE IMAGEM; DIAGNÓSTICO POR COMPUTADOR
- Keywords: Deep Learning; Adversarial Attacks; Security & Privacy; Medical Images
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2023
- Source:
- Título do periódico: Proceedings
- ISSN: 2372-9198
- Conference titles: 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
AGUIAR, Erikson Júlio de et al. Assessing vulnerabilities of deep learning explainability in medical image analysis under adversarial settings. 2023, Anais.. Los Alamitos: IEEE, 2023. Disponível em: https://doi.org/10.1109/CBMS58004.2023.00184. Acesso em: 27 set. 2024. -
APA
Aguiar, E. J. de, Costa, M. V. L., Traina Junior, C., & Traina, A. J. M. (2023). Assessing vulnerabilities of deep learning explainability in medical image analysis under adversarial settings. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS58004.2023.00184 -
NLM
Aguiar EJ de, Costa MVL, Traina Junior C, Traina AJM. Assessing vulnerabilities of deep learning explainability in medical image analysis under adversarial settings [Internet]. Proceedings. 2023 ;[citado 2024 set. 27 ] Available from: https://doi.org/10.1109/CBMS58004.2023.00184 -
Vancouver
Aguiar EJ de, Costa MVL, Traina Junior C, Traina AJM. Assessing vulnerabilities of deep learning explainability in medical image analysis under adversarial settings [Internet]. Proceedings. 2023 ;[citado 2024 set. 27 ] Available from: https://doi.org/10.1109/CBMS58004.2023.00184 - Security and privacy in machine learning for health systems: strategies and challenges
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Informações sobre o DOI: 10.1109/CBMS58004.2023.00184 (Fonte: oaDOI API)
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