Análise de Desaprendizado de Máquina em Modelos de Classificação de Imagens Médicas
Resumo
O desaprendizado de máquina tem como objetivo remover dados privados ou sensíveis de um modelo pré-treinado, preservando a robustez do modelo. Apesar dos avanços, essa técnica não tem sido explorada em classificação de imagens médicas. Esse trabalho avalia o modelo de desaprendizagem Salun, conduzindo experimentos nas bases PathMNIST, OrganAMNIST e BloodMNIST. Também analisamos a influência do aumento de dados na qualidade do desaprendizado. Resultados mostram que o Salun obtém resultados próximos ao retreinamento completo, indicando uma solução eficiente para ser usada em aplicações médicas.Referências
Chan, H.-P., Samala, R. K., Hadjiiski, L. M., and Zhou, C. (2020). Deep learning in medical image analysis. Deep learning in medical image analysis: challenges and applications, pages 3–21.
Dang, Q.-V. (2021). Right to be forgotten in the age of machine learning. In Advances in Digital Science: ICADS 2021, pages 403–411. Springer.
Di, Z., Zhu, Z., Jia, J., Liu, J., Takhirov, Z., Jiang, B., Yao, Y., Liu, S., and Liu, Y. (2024). Label smoothing improves machine unlearning. arXiv preprint arXiv:2406.07698.
Fan, C., Liu, J., Zhang, Y., Wong, E., Wei, D., and Liu, S. (2024). Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation. In International Conference on Learning Representations (ICLR).
Golatkar, A., Achille, A., and Soatto, S. (2020). Eternal sunshine of the spotless net: Selective forgetting in deep networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9304–9312.
Graves, L., Nagisetty, V., and Ganesh, V. (2021). Amnesiac machine learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 11516–11524.
Hoofnagle, C. J., Van Der Sloot, B., and Borgesius, F. Z. (2019). The european union general data protection regulation: what it is and what it means. Information & Communications Technology Law, 28(1):65–98.
Jia, J., Liu, J., Ram, P., Yao, Y., Liu, G., Liu, Y., Sharma, P., and Liu, S. (2023). Model sparsity can simplify machine unlearning. Advances in Neural Information Processing Systems, 36:51584–51605.
Mumuni, A. and Mumuni, F. (2022). Data augmentation: A comprehensive survey of modern approaches. Array, 16:100258.
Wu, Z., Shen, C., and Van Den Hengel, A. (2019). Wider or deeper: Revisiting the resnet model for visual recognition. Pattern recognition, 90:119–133.
Yang, J., Shi, R., Wei, D., Liu, Z., Wang, L., Zhou, Y., Zhou, S., Bian, C., Li, L., Wang, X., et al. (2021). Medmnist: A lightweight automl benchmark for medical image analysis. [link]. Accessed: February 13, 2025.
Zhang, H., Nakamura, T., Isohara, T., and Sakurai, K. (2023). A review on machine unlearning. SN Computer Science, 4(4):337.
Dang, Q.-V. (2021). Right to be forgotten in the age of machine learning. In Advances in Digital Science: ICADS 2021, pages 403–411. Springer.
Di, Z., Zhu, Z., Jia, J., Liu, J., Takhirov, Z., Jiang, B., Yao, Y., Liu, S., and Liu, Y. (2024). Label smoothing improves machine unlearning. arXiv preprint arXiv:2406.07698.
Fan, C., Liu, J., Zhang, Y., Wong, E., Wei, D., and Liu, S. (2024). Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation. In International Conference on Learning Representations (ICLR).
Golatkar, A., Achille, A., and Soatto, S. (2020). Eternal sunshine of the spotless net: Selective forgetting in deep networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9304–9312.
Graves, L., Nagisetty, V., and Ganesh, V. (2021). Amnesiac machine learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 11516–11524.
Hoofnagle, C. J., Van Der Sloot, B., and Borgesius, F. Z. (2019). The european union general data protection regulation: what it is and what it means. Information & Communications Technology Law, 28(1):65–98.
Jia, J., Liu, J., Ram, P., Yao, Y., Liu, G., Liu, Y., Sharma, P., and Liu, S. (2023). Model sparsity can simplify machine unlearning. Advances in Neural Information Processing Systems, 36:51584–51605.
Mumuni, A. and Mumuni, F. (2022). Data augmentation: A comprehensive survey of modern approaches. Array, 16:100258.
Wu, Z., Shen, C., and Van Den Hengel, A. (2019). Wider or deeper: Revisiting the resnet model for visual recognition. Pattern recognition, 90:119–133.
Yang, J., Shi, R., Wei, D., Liu, Z., Wang, L., Zhou, Y., Zhou, S., Bian, C., Li, L., Wang, X., et al. (2021). Medmnist: A lightweight automl benchmark for medical image analysis. [link]. Accessed: February 13, 2025.
Zhang, H., Nakamura, T., Isohara, T., and Sakurai, K. (2023). A review on machine unlearning. SN Computer Science, 4(4):337.
Publicado
09/06/2025
Como Citar
FALCAO, Andreza M. C.; CORDEIRO, Filipe R..
Análise de Desaprendizado de Máquina em Modelos de Classificação de Imagens Médicas. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 43-48.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2025.6966.
