Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction.

ABSTRACT: In imaging inverse problems, one seeks to recover an image from missing/corrupted measurements. Because such problems are ill-posed, there is great motivation to quantify the uncertainty induced by the measurement-and-recovery process. Motivated by applications where the recovered image is used for a downstream task, such as soft-output classification, we propose a task-centered approach to uncertainty quantification. In particular, we use conformal prediction to construct an interval that is guaranteed to contain the task output from the true image up to a user-specified probability, and we use the width of that interval to quantify the uncertainty contributed by measurement-and-recovery. For posterior-sampling-based image recovery, we construct locally adaptive prediction intervals. Furt

SUBMITTER: Wen J 

PROVIDER: S-EPMC12109201 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature