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
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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: 03 jan. 2026.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/BigData62323.2024.10826044NLM
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 2026 jan. 03 ] Available from: https://doi.org/10.1109/BigData62323.2024.10826044Vancouver
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 2026 jan. 03 ] Available from: https://doi.org/10.1109/BigData62323.2024.10826044
