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Trabalho de evento
AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data (2025)
Silva, João Pedro da; Aguiar, Erikson Júlio de ; Spadon, Gabriel ; Traina, Agma Juci Machado ; Rodrigues Junior, José Fernando
Source: Proceedings. Conference titles: IEEE International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, SENSORIAMENTO REMOTO, INDICADORES SOCIOECONÔMICOS, SAÚDE PÚBLICA, BRASIL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, João Pedro da et al. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data. 2025, Anais.. Los Alamitos: IEEE, 2025. Disponível em: https://doi.org/10.1109/CBMS65348.2025.00180. Acesso em: 08 out. 2025.
APA
Silva, J. P. da, Aguiar, E. J. de, Spadon, G., Traina, A. J. M., & Rodrigues Junior, J. F. (2025). AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS65348.2025.00180
NLM
Silva JP da, Aguiar EJ de, Spadon G, Traina AJM, Rodrigues Junior JF. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data [Internet]. Proceedings. 2025 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00180
Vancouver
Silva JP da, Aguiar EJ de, Spadon G, Traina AJM, Rodrigues Junior JF. AI-driven public health surveillance: analyzing vulnerable areas in Brazil using remote sensing and socioeconomic data [Internet]. Proceedings. 2025 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/CBMS65348.2025.00180
Trabalho de evento
RADAR-MIX: how to uncover adversarial attacks in medical image analysis through explainability (2024)
Aguiar, Erikson Júlio de ; Traina Junior, Caetano ; Traina, Agma Juci Machado
Source: Proceedings. Conference titles: IEEE International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Subjects: BANCO DE DADOS, VISUALIZAÇÃO, APRENDIZAGEM PROFUNDA, TECNOLOGIAS DA SAÚDE, DIAGNÓSTICO POR IMAGEM
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
AGUIAR, Erikson Júlio de e TRAINA JUNIOR, Caetano e TRAINA, Agma Juci Machado. RADAR-MIX: how to uncover adversarial attacks in medical image analysis through explainability. 2024, Anais.. Los Alamitos: IEEE, 2024. Disponível em: https://doi.org/10.1109/CBMS61543.2024.00078. Acesso em: 08 out. 2025.
APA
Aguiar, E. J. de, Traina Junior, C., & Traina, A. J. M. (2024). RADAR-MIX: how to uncover adversarial attacks in medical image analysis through explainability. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS61543.2024.00078
NLM
Aguiar EJ de, Traina Junior C, Traina AJM. RADAR-MIX: how to uncover adversarial attacks in medical image analysis through explainability [Internet]. Proceedings. 2024 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/CBMS61543.2024.00078
Vancouver
Aguiar EJ de, Traina Junior C, Traina AJM. RADAR-MIX: how to uncover adversarial attacks in medical image analysis through explainability [Internet]. Proceedings. 2024 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/CBMS61543.2024.00078
Trabalho de evento
MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data (2024)
Aguiar, Erikson Júlio de ; Traina, Agma Juci Machado ; Helal, Sumi
Source: Proceedings. Conference titles: IEEE International Conference on Big Data - Big Data. Unidade: ICMC
Subjects: BANCO DE DADOS MULTIMÍDIA, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR IMAGEM, TECNOLOGIAS DA SAÚDE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
AGUIAR, Erikson Júlio de e TRAINA, Agma Juci Machado e HELAL, Sumi. MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data. 2024, Anais.. Piscataway: IEEE, 2024. Disponível em: https://doi.org/10.1109/BigData62323.2024.10826044. Acesso em: 08 out. 2025.
APA
Aguiar, E. J. de, Traina, A. J. M., & Helal, S. (2024). MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data. In Proceedings. Piscataway: IEEE. doi:10.1109/BigData62323.2024.10826044
NLM
Aguiar EJ de, Traina AJM, Helal S. MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data [Internet]. Proceedings. 2024 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/BigData62323.2024.10826044
Vancouver
Aguiar EJ de, Traina AJM, Helal S. MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data [Internet]. Proceedings. 2024 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/BigData62323.2024.10826044
Trabalho de evento
Assessing vulnerabilities of deep learning explainability in medical image analysis under adversarial settings (2023)
Aguiar, Erikson Júlio de ; Costa, Márcus Vinícius Lobo ; Traina Junior, Caetano ; Traina, Agma Juci Machado
Source: Proceedings. Conference titles: International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, PROCESSAMENTO DE IMAGENS, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR COMPUTADOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 08 out. 2025.
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 2025 out. 08 ] 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 2025 out. 08 ] Available from: https://doi.org/10.1109/CBMS58004.2023.00184
Trabalho de evento
A deep learning-based radiomics approach for COVID-19 detection from CXR images using ensemble learning model (2023)
Costa, Márcus Vinícius Lobo ; Aguiar, Erikson Júlio de ; Rodrigues, Lucas Santiago ; Ramos, Jonathan da Silva ; Traina Junior, Caetano ; Traina, Agma Juci Machado
Source: Proceedings. Conference titles: International Symposium on Computer-Based Medical Systems - CBMS. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, PROCESSAMENTO DE IMAGENS, RECONHECIMENTO DE IMAGEM, DIAGNÓSTICO POR COMPUTADOR, TECNOLOGIAS DA SAÚDE, COVID-19
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COSTA, Márcus Vinícius Lobo et al. A deep learning-based radiomics approach for COVID-19 detection from CXR images using ensemble learning model. 2023, Anais.. Los Alamitos: IEEE, 2023. Disponível em: https://doi.org/10.1109/CBMS58004.2023.00272. Acesso em: 08 out. 2025.
APA
Costa, M. V. L., Aguiar, E. J. de, Rodrigues, L. S., Ramos, J. da S., Traina Junior, C., & Traina, A. J. M. (2023). A deep learning-based radiomics approach for COVID-19 detection from CXR images using ensemble learning model. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS58004.2023.00272
NLM
Costa MVL, Aguiar EJ de, Rodrigues LS, Ramos J da S, Traina Junior C, Traina AJM. A deep learning-based radiomics approach for COVID-19 detection from CXR images using ensemble learning model [Internet]. Proceedings. 2023 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/CBMS58004.2023.00272
Vancouver
Costa MVL, Aguiar EJ de, Rodrigues LS, Ramos J da S, Traina Junior C, Traina AJM. A deep learning-based radiomics approach for COVID-19 detection from CXR images using ensemble learning model [Internet]. Proceedings. 2023 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/CBMS58004.2023.00272
Trabalho de evento
Analysis of vertebrae without fracture on spine MRI to assess bone fragility: a comparison of traditional machine learning and deep learning (2022)
Ramos, Jonathan da Silva ; Aguiar, Erikson Júlio de ; Belizario, Ivar Vargas ; Costa, Márcus Vinícius Lobo ; Maciel, Jamilly Gomes; Cazzolato, Mirela Teixeira ; Traina Junior, Caetano ; Nogueira-Barbosa, Marcello Henrique ; Traina, Agma Juci Machado
Source: Proceedings. Conference titles: International Symposium on Computer-Based Medical Systems - CBMS. Unidades: ICMC, FMRP
Subjects: APRENDIZADO COMPUTACIONAL, TECNOLOGIAS DA SAÚDE, RESSONÂNCIA MAGNÉTICA, DIAGNÓSTICO POR IMAGEM, OSTEOGÊNESE IMPERFEITA, COLUNA VERTEBRAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RAMOS, Jonathan da Silva et al. Analysis of vertebrae without fracture on spine MRI to assess bone fragility: a comparison of traditional machine learning and deep learning. 2022, Anais.. Los Alamitos: IEEE, 2022. Disponível em: https://doi.org/10.1109/CBMS55023.2022.00021. Acesso em: 08 out. 2025.
APA
Ramos, J. da S., Aguiar, E. J. de, Belizario, I. V., Costa, M. V. L., Maciel, J. G., Cazzolato, M. T., et al. (2022). Analysis of vertebrae without fracture on spine MRI to assess bone fragility: a comparison of traditional machine learning and deep learning. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS55023.2022.00021
NLM
Ramos J da S, Aguiar EJ de, Belizario IV, Costa MVL, Maciel JG, Cazzolato MT, Traina Junior C, Nogueira-Barbosa MH, Traina AJM. Analysis of vertebrae without fracture on spine MRI to assess bone fragility: a comparison of traditional machine learning and deep learning [Internet]. Proceedings. 2022 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/CBMS55023.2022.00021
Vancouver
Ramos J da S, Aguiar EJ de, Belizario IV, Costa MVL, Maciel JG, Cazzolato MT, Traina Junior C, Nogueira-Barbosa MH, Traina AJM. Analysis of vertebrae without fracture on spine MRI to assess bone fragility: a comparison of traditional machine learning and deep learning [Internet]. Proceedings. 2022 ;[citado 2025 out. 08 ] Available from: https://doi.org/10.1109/CBMS55023.2022.00021

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