Bias-Aware Conformal Prediction for Metric-Based Imaging Pipelines

TL;DR

This paper investigates how bias affects conformal prediction intervals in medical imaging, proposing bias-aware methods to improve confidence measures in imaging pipelines for better clinical decision-making.

Contribution

It formalizes the impact of bias on symmetric and asymmetric conformal prediction intervals and provides guidelines for selecting optimal formulations in medical imaging applications.

Findings

Symmetric intervals are inflated by twice the bias magnitude.

Asymmetric intervals are unaffected by bias.

Empirical validation on CT reconstruction confirms theoretical results.

Abstract

Reliable confidence measures of metrics derived from medical imaging reconstruction pipelines would improve the standard of decision-making in many clinical workflows. Conformal Prediction (CP) provides a robust framework for producing calibrated prediction intervals, but standard CP formulations face a critical challenge in the imaging pipeline: common mismatches between image reconstruction objectives and downstream metrics can introduce systematic prediction deviations from ground truth values, known as bias. These biases in turn compromise the efficiency of prediction intervals, which is a problem that has been unexplored in the CP literature. In this study, we formalize the behavior of symmetric (where bounds expand equally in both directions) and asymmetric (where bounds expand unequally) formulations for common non-conformity scores in CP in the presence of bias, and argue that this measurable bias must inform the choice of CP formulation. We theoretically and empirically demonstrate that symmetric intervals are inflated by a factor of two times the magnitude of bias while asymmetric intervals remain unaffected by bias, and provide conditions under which each formulation produces tighter intervals. We empirically validated our theoretical analyses on sparse-view CT reconstruction for downstream radiotherapy planning. Our work enables users of medical imaging pipelines to proactively select optimal CP formulations, thereby improving interval length efficiency for critical downstream metrics.