Machine unlearning is the process of removing the impact of a particular set of training samples from a pretrained model. It aims to fulfill the "right to be forgotten", which grants the individuals such as patients the right to reconsider their contribution in models including medical imaging models. In this study, we evaluate the effectiveness (performance) and computational efficiency of different unlearning algorithms in medical imaging domain. Our evaluations demonstrate that the considered unlearning algorithms perform well on the retain set (samples whose influence on the model is allowed to be retained) and forget set (samples whose contribution to the model should be eliminated), and show no bias against male or female samples. They, however, adversely impact the generalization of the model, especially for larger forget set sizes. Moreover, they might be biased against easy or hard samples, and need additional computational overhead for hyper-parameter tuning. In conclusion, machine unlearning seems promising for medical imaging, but the existing unlearning algorithms still needs further improvements to become more practical for medical applications.

翻译：机器遗忘是指从预训练模型中移除特定训练样本影响的过程。其旨在实现"被遗忘权"，该权利赋予患者等个体重新考虑其对医学影像模型等模型贡献的权利。本研究评估了医学影像领域中不同遗忘算法的有效性（性能）与计算效率。评估结果表明，所考察的遗忘算法在保留集（允许保留其对模型影响的样本）与遗忘集（应消除其对模型贡献的样本）上表现良好，且未对男性或女性样本表现出偏差。然而，这些算法会对模型的泛化能力产生负面影响，在遗忘集规模较大时尤为显著。此外，它们可能对简单或困难样本存在偏差，且需要额外的计算开销进行超参数调优。综上所述，机器遗忘在医学影像领域前景可观，但现有遗忘算法仍需进一步改进才能更适用于医疗实践。