Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912892319694848 |
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| author | Nahass, George R. Wang, Zhu Rashidisabet, Homa Kim, Won Hwa Hubschman, Sasha Peterson, Jeffrey C. Purnell, Chad A. Setabutr, Pete Tran, Ann Q. Yi, Darvin Ravi, Sathya N. |
| author_facet | Nahass, George R. Wang, Zhu Rashidisabet, Homa Kim, Won Hwa Hubschman, Sasha Peterson, Jeffrey C. Purnell, Chad A. Setabutr, Pete Tran, Ann Q. Yi, Darvin Ravi, Sathya N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21872 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images Nahass, George R. Wang, Zhu Rashidisabet, Homa Kim, Won Hwa Hubschman, Sasha Peterson, Jeffrey C. Purnell, Chad A. Setabutr, Pete Tran, Ann Q. Yi, Darvin Ravi, Sathya N. Image and Video Processing Machine Learning 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. |
| title | Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2505.21872 |