Erikson Julio de Aguiar, Êrica Peters do Carmo, Agma Juci Machado Traina, and Caetano Traina Junior
Security and Privacy in Data-Driven Systems: From Traditional Machine Learning to Large Language Models
Anais De Minicursos Do Simpósio Brasileiro De Banco De Dados (SBBD), 1–28. https://doi.org/10.5753/sbc.20981.0.1. (2026)
Pedro Fuziwara Filho, Erikson Julio de Aguiar, and Agma Juci Machado Traina
A Cross-Lingual Security Evaluation of Prompt Injection Defenses in Small Language Models
XXVI Simpósio Brasileiro de Cibersegurança, Workshop de Cibersegurança em IA (WCIA) (2026). Pre-Print.
Graciella Favoreto Dos Santos, Mariana Aya Suzuki Uchida, Pedro Augusto Luiz, Erikson Julio de Aguiar, Marcel Koenigkam Santos, and Agma Juci Machado Traina
A Pipeline for Context vs. Segmented ROI Images in Lung Disease Classification through Saliency Map Insights
Revista De Informática Teórica E Aplicada, 33(2), 73–80. https://doi.org/10.22456/2175-2745.150955 (2026)
Mariana Aya Suzuki Uchida, Erikson Julio de Aguiar, Caetano Traina Jr., and Agma Juci Machado Traina
Data Augmentation for Medical Image Segmentation: A Comparative Analysis of Traditional Techniques and Synthetic Data Generation
Anais do XL Simpósio Brasileiro de Bancos de Dados (SSBD), Fortaleza/CE, Brazil, pp. 896-901, ddoi:10.5753/sbbd.2025.247731 (2025)
João Pedro Silva, Erikson Julio de Aguiar, Gabriel Spadon, Agma Traina, and José Fernando Rodrigues Jr.
AI-Driven Public Health Surveillance: Analyzing Vulnerable Areas in Brazil Using Remote Sensing and Socioeconomic Data
IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), Madrid, Spain, pp. 896-901, doi: 10.1109/CBMS65348.2025.00180 (2025)
Erikson Julio de Aguiar, Agma Traina, and Sumi Helal
SentinelAdvMedical: Toward adversarial attacks detection on medical image classification via out-of-distribution strategies
SPIE Medical Imaging, 2025, San Diego, California, United States, pp. 1-7, doi:10.1117/12.3046951. (2025)
Erikson Julio de Aguiar, Agma Traina, and Sumi Helal
MedTimeSplit: Continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data
IEEE International Conference on Big Data (BigData), Washington, DC, USA, 2024, pp. 7612-7621, doi: 10.1109/BigData62323.2024.10826044 (2024)
Erikson Julio de Aguiar, Caetano Traina Jr., and Agma Traina
RADAR-MIX: How to Uncover Adversarial Attacks in Medical Image Analysis through Explainability
IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS), Guadalajara, Mexico, pp. 436-441, doi: 10.1109/CBMS61543.2024.00078. (2024) - Best Student Paper Award
Erikson Julio de Aguiar, Marcus Costa, Caetano Traina Jr., and Agma Traina
Assessing Vulnerabilities of Deep Learning Explainability in Medical Image Analysis Under Adversarial Settings
IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), L'Aquila, Italy, pp. 13-16, doi: 10.1109/CBMS58004.2023.00184. (2023)
Marcus Costa, Erikson Julio de Aguiar, Jonathan Ramos, Lucas Rodrigues, Caetano Traina Jr., and Agma Traina
A Deep Learning-based Radiomics Approach for COVID-19 Detection from CXR Images using Ensemble Learning Model
IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), L'Aquila, Italy, pp. 517-522, doi: 10.1109/CBMS58004.2023.00272. (2023)
Jonathan Ramos, Erikson Julio de Aguiar, Ivar Belizario, Marcus Costa, Jamily Maciel, Mirela Cazzolato, Caetano Traina Jr., Marcelo Nogueira-Barbosa, and Agma Traina
Analysis of vertebrae without fracture on spine MRI to assess bone fragility: A Comparison of Traditional Machine Learning and Deep Learning
IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), Shenzen, China, pp. 78-83, doi: 10.1109/CBMS55023.2022.00021. (2022)
Erikson Julio de Aguiar, Karen Marcomini, Marco Gutierrez, Caetano Traina Jr., and Agma Traina
Evaluation of the Impact of Physical Adversarial Attacks on Deep Learning Models for Classifying COVID Cases
SPIE Medical Imaging, 2022, San Diego, California, United States, pp. 722-728, doi:10.1117/12.2611199 (2022)
Erikson Julio de Aguiar, Bruno Faiçal, Glauco Carlos, and André Menolli
Análise de Sentimento em Redes Sociais para a Língua Portuguesa Utilizando Algoritmos de Classificação
XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), Campus do Jordão, Brazil, pp. 393-406, doi:10.5753/sbrc.2018.2430 (2018)
Erikson Julio de Aguiar, Sumi Helal, Caetano Traina Jr., and Agma Traina
Sentinel-MIX: Explainability-Driven Adversarial Attacks Detection with Focus on Skin Lesion Classification
IEEE Access. (Under Review)
Marcus Minicius Lobo Costa, Erikson Julio de Aguiar, Caetano Traina Jr., and Agma Traina
DEELE-Rad: exploiting deep radiomics features in deep learning models using COVID-19 chest X-ray images
Health Information Science and Systems, pp. 1-15, doi: 10.1007/s13755-024-00330-6 (2024)
Erikson Julio de Aguiar, Caetano Traina Jr., and Agma Traina
Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges.
IMIA Yearbook of Medical Informatics, 32(01): 269-28. DOI: 10.1055/s-0043-1768731 (2023)
Erikson Julio de Aguiar, Alyson Dos Santos, Rodolfo Meneguette, Robson De Grande, and Jó Ueyama
A blockchain-based protocol for tracking user access to shared medical imaging
Future Generation Computer Systems, 134, pp.348–360, doi:10.1016/j.future.2022.04.017 (2022)
Erikson Julio de Aguiar, Bruno Faiçal, Baskar Krishnamachari, and Jó Ueyama
A Survey of Blockchain-Based Strategies for Healthcare
ACM Computing Surveys, 53(2), pp.1–27, doi:10.1145/3376915 (2020)