提出在医学图像分类中高效删除敏感数据的新方法。
Analise de Desaprendizado de Maquina em Modelos de Classificacao de Imagens Medicas
- 采用SalUn模型实现医疗图像模型的数据删除。
- 性能接近完整重训练，验证了有效性。
- 适合需要隐私保护的医疗AI应用场景。
机器遗忘旨在移除预训练模型中的私有或敏感数据，同时保持模型鲁棒性。尽管近期取得进展，该技术尚未在医学图像分类中得到探索。本文在PathMNIST、OrganAMNIST和BloodMNIST数据集上评估SalUn遗忘模型，并分析数据增强对遗忘质量的影响。结果表明，SalUn性能接近完整重训练，证明其在医疗应用中具有高效性。
原文摘要 · Abstract (English)
Machine unlearning aims to remove private or sensitive data from a pre-trained model while preserving the model's robustness. Despite recent advances, this technique has not been explored in medical image classification. This work evaluates the SalUn unlearning model by conducting experiments on the PathMNIST, OrganAMNIST, and BloodMNIST datasets. We also analyse the impact of data augmentation on the quality of unlearning. Results show that SalUn achieves performance close to full retraining, indicating an efficient solution for use in medical applications.
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