提出新方法让医疗模型主动遗忘患者数据，保护隐私且不影响其他预测性能。
Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settings
- 通过损失函数与扰动技术，同时清除多模态数据的单模态和联合表征。
- 忘记数据集上AUC和F1分别下降0.221和0.305，攻击者识别能力降低0.202。
- 保持测试集性能接近重训练模型，适合需合规删除数据的医疗AI场景。
医疗AI中隐私保护至关重要，模型依赖敏感患者数据。现有机器遗忘方法难以从多模态架构中移除患者数据，而此类架构在医疗领域广泛应用。本文提出Forget-MI，一种针对多模态医疗数据的新型机器遗忘方法，通过构建损失函数与扰动技术，实现对被遗忘数据的单模态与联合表征的消除，同时保留剩余数据的知识，并维持与原始模型相当的性能。评估基于遗忘数据集表现、测试集表现及成员推断攻击（MIA）能力。结果表明，该方法在降低MIA（减少0.202）的同时，使遗忘数据集的AUC与F1得分分别下降0.221和0.305，且测试集性能与重新训练模型相当。代码已开源。
原文摘要 · Abstract (English)
Privacy preservation in AI is crucial, especially in healthcare, where models rely on sensitive patient data. In the emerging field of machine unlearning, existing methodologies struggle to remove patient data from trained multimodal architectures, which are widely used in healthcare. We propose Forget-MI, a novel machine unlearning method for multimodal medical data, by establishing loss functions and perturbation techniques. Our approach unlearns unimodal and joint representations of the data requested to be forgotten while preserving knowledge from the remaining data and maintaining comparable performance to the original model. We evaluate our results using performance on the forget dataset, performance on the test dataset, and Membership Inference Attack (MIA), which measures the attacker's ability to distinguish the forget dataset from the training dataset. Our model outperforms the existing approaches that aim to reduce MIA and the performance on the forget dataset while keeping an equivalent performance on the test set. Specifically, our approach reduces MIA by 0.202 and decreases AUC and F1 scores on the forget set by 0.221 and 0.305, respectively. Additionally, our performance on the test set matches that of the retrained model, while allowing forgetting. Code is available at https://github.com/BioMedIA-MBZUAI/Forget-MI.git
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