Treinamento Federado Aplicado à Segmentação do Ventrículo Esquerdo
Resumo
A natureza sensível dos dados médicos é um desafio para a utilização de modelos centralizados de Aprendizado de Máquina (Machine Learning - ML). Em contraste com o ML tradicional, o Aprendizado Federado (Federated Learning - FL) permite que modelos sejam treinados entre instituições sem o compartilhamento de dados. Dessa forma, este artigo apresenta uma análise comparativa do uso de um modelo tradicional de ML para a segmentação de imagens médicas em comparação com o paradigma de FL, destacando seus benefícios no desenvolvimento de modelos colaborativos.Referências
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., and Lane, N. D. (2020). Flower: A Friendly Federated Learning Research Framework. arXiv, abs/2007.14390.
Bochie, K., Sammarco, M., Detyniecki, M., and Campista, M. (2021). Análise do Aprendizado Federado em Redes Móveis. In Anais do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 71–84, Porto Alegre, RS, Brasil. SBC.
Dou, Q., So, T. Y., Jiang, M., Liu, Q., Vardhanabhuti, V., Kaissis, G., Li, Z., Si, W., Lee, H. H. C., Yu, K., Feng, Z., Dong, L., Burian, E., Jungmann, F., Braren, R., Makowski, M., Kainz, B., Rueckert, D., Glocker, B., Yu, S. C. H., and Heng, P. A. (2021). Federated Deep Learning for Detecting COVID-19 Lung Abnormalities in CT: a Privacy-Preserving Multinational Validation Study. Digital Medicine.
Flores, M., Dayan, I., Roth, H., Zhong, A., Harouni, A., Gentili, A., Abidin, A., Liu, A., Costa, A., Wood, B., Tsai, C., CH Wang, C. H., and Wen, Y. (2021). Federated Learning used for Predicting Outcomes in SARS-COV-2 Patients. Nat Med.
Joshi, M., Pal, A., and Sankarasubbu, M. (2022). Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges. ACM Trans. Comput. Healthc.
Li, M., Soltanolkotabi, M., and Oymak, S. (2020). Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks. In International Conference on Artificial Intelligence and Statistics, pages 4313–4324. PMLR.
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., and He, B. (2021). A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. IEEE Transactions on Knowledge and Data Engineering.
Li, W., Milletarı̀, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019). Privacy-preserving Federated Brain Tumour Segmentation. In Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10, pages 133–141. Springer.
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., and Miao, C. (2020). Federated Learning in Mobile Edge Networks: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 22(3):2031–2063.
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2023). Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv.
Ouyang, D., He, B., Ghorbani, A., Lungren, M. P., Ashley, E. A., Liang, D. H., and Zou, J. Y. (2019). EchoNet-Dynamic: a Large New Cardiac Motion Video Data Resource for Medical Machine Learning. Computer Science, Medicine.
Bochie, K., Sammarco, M., Detyniecki, M., and Campista, M. (2021). Análise do Aprendizado Federado em Redes Móveis. In Anais do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 71–84, Porto Alegre, RS, Brasil. SBC.
Dou, Q., So, T. Y., Jiang, M., Liu, Q., Vardhanabhuti, V., Kaissis, G., Li, Z., Si, W., Lee, H. H. C., Yu, K., Feng, Z., Dong, L., Burian, E., Jungmann, F., Braren, R., Makowski, M., Kainz, B., Rueckert, D., Glocker, B., Yu, S. C. H., and Heng, P. A. (2021). Federated Deep Learning for Detecting COVID-19 Lung Abnormalities in CT: a Privacy-Preserving Multinational Validation Study. Digital Medicine.
Flores, M., Dayan, I., Roth, H., Zhong, A., Harouni, A., Gentili, A., Abidin, A., Liu, A., Costa, A., Wood, B., Tsai, C., CH Wang, C. H., and Wen, Y. (2021). Federated Learning used for Predicting Outcomes in SARS-COV-2 Patients. Nat Med.
Joshi, M., Pal, A., and Sankarasubbu, M. (2022). Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges. ACM Trans. Comput. Healthc.
Li, M., Soltanolkotabi, M., and Oymak, S. (2020). Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks. In International Conference on Artificial Intelligence and Statistics, pages 4313–4324. PMLR.
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., and He, B. (2021). A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. IEEE Transactions on Knowledge and Data Engineering.
Li, W., Milletarı̀, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019). Privacy-preserving Federated Brain Tumour Segmentation. In Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10, pages 133–141. Springer.
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., and Miao, C. (2020). Federated Learning in Mobile Edge Networks: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 22(3):2031–2063.
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2023). Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv.
Ouyang, D., He, B., Ghorbani, A., Lungren, M. P., Ashley, E. A., Liang, D. H., and Zou, J. Y. (2019). EchoNet-Dynamic: a Large New Cardiac Motion Video Data Resource for Medical Machine Learning. Computer Science, Medicine.
Publicado
07/12/2023
Como Citar
DA SILVA, Vinicios B.; DE OLIVEIRA, Renan R.; OLIVEIRA-JR, Antonio; DA COSTA, Ronaldo M..
Treinamento Federado Aplicado à Segmentação do Ventrículo Esquerdo. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 11. , 2023, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
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DOI: https://doi.org/10.5753/erigo.2023.237317.
