Machine unlearning aims to remove the influence of specific training samples from a trained model without full retraining. While prior work has largely focused on privacy-motivated settings, we recast unlearning as a general-purpose tool for post-deployment model revision. Specifically, we focus on utilizing unlearning in clinical contexts where data shifts, device deprecation, and policy changes are common. To this end, we propose a bilevel optimization formulation of boundary-based unlearning that can be solved using iterative algorithms. We provide convergence guarantees when first-order algorithms are used to unlearn. Our method introduces tunable loss design for controlling the forgetting-retention tradeoff and supports novel model composition strategies that merge the strengths of distinct unlearning runs. Across benchmark and real-world clinical imaging datasets, our approach outperforms baselines on both forgetting and retention metrics, including scenarios involving imaging devices and anatomical outliers. This work establishes machine unlearning as a modular, practical alternative to retraining for real-world model maintenance in clinical applications.

翻译：机器遗忘旨在无需完全重新训练的情况下，从已训练模型中移除特定训练样本的影响。先前的研究主要关注隐私驱动的场景，而我们将遗忘重新定位为一种用于部署后模型修正的通用工具。具体而言，我们专注于在数据偏移、设备淘汰和政策变更常见的临床环境中利用遗忘技术。为此，我们提出了一种基于边界的遗忘双层优化公式，该公式可通过迭代算法求解。我们证明了使用一阶算法进行遗忘时的收敛性保证。我们的方法引入了可调损失设计以控制遗忘与保留的权衡，并支持新颖的模型组合策略，以融合不同遗忘运行的优势。在基准数据集和真实世界临床影像数据集上的实验表明，我们的方法在遗忘和保留指标上均优于基线模型，包括涉及成像设备和解剖异常的场景。这项工作确立了机器遗忘作为一种模块化、实用的替代方案，可用于临床应用中真实世界模型的维护，以替代重新训练。