Towards Certified Shortcut Unlearning in Medical Imaging

Many medical AI imaging applications rely on segmentation-classification pipelines. In practice, coarse or imprecise segmentation masks often admit *spurious* background cues and the model learns the associations between these cues and the pathological regions of interest (organs, lesions, etc.). Such *shortcuts* tend to severely affect the accuracy of the downstream task. Prior art relies on fine-grained pixel-level annotations to separate background cues from pathology regions. Unfortunately, high-quality annotations are expensive. We aim to ensure high performance on downstream tasks without relying on expensive annotations. We propose using machine unlearning to mitigate shortcut learning. In contrast to relying on heuristic methods, we propose a principled approach that, for the first time, provides guarantees for shortcut unlearning. We develop a definition of certified unlearning for segmentation tasks and formally connect it to its canonical definition. Further, we introduce an information-theoretic objective function, *global information reduction*, and prove that any $(\epsilon ,\delta )$-certified machine unlearning operator bounds it relative to the fine-mask reference. Importantly, the bound further limits the accuracy any decision rule can extract from the artefact. We discover existing certified operators collapse at the scale of real medical decoders, and introduce a new machine unlearning algorithm that challenges the SOTA in segmentation at tight $(\epsilon ,\delta )$ budgets. On ISIC melanoma trap sets and SIIM-ACR pneumothorax with a naturally occurring chest-tube shortcut, we compare unlearning with state-of-the-art refinement methods on segmentation, shortcut robustness, fine-mask budget and compute. Unlearning spans the Pareto front on both datasets, no refinement method reaches it, and the certified operator is ahead of exact retraining, the only other method with a guarantee, at every fine-mask budget.