CARE: Confidence-aware Ratio Estimation for Medical Biomarkers

TL;DR

This paper introduces CARE, a framework that provides confidence intervals for ratio-based biomarkers derived from medical image segmentation, enhancing trustworthiness in clinical decision-making.

Contribution

It offers a unified method to quantify uncertainty in ratio-based biomarkers by analyzing error propagation and model calibration, which was previously lacking.

Findings

CARE produces statistically sound confidence intervals for biomarkers.

The method identifies model miscalibration as the main source of uncertainty.

Experiments demonstrate improved trustworthiness in clinical applications.

Abstract

Ratio-based biomarkers (RBBs), such as the proportion of necrotic tissue within a tumor, are widely used in clinical practice to support diagnosis, prognosis, and treatment planning. These biomarkers are typically estimated from segmentation outputs by computing region-wise ratios. Despite the high-stakes nature of clinical decision making, existing methods provide only point estimates, offering no measure of uncertainty. In this work, we propose a unified confidence-aware framework for estimating ratio-based biomarkers. Our uncertainty analysis stems from two observations: (1) the probability ratio estimator inherently admits a statistical confidence interval regarding local randomness (bias and variance); (2) the segmentation network is not perfectly calibrated (calibration error).We perform a systematic analysis of error propagation in the segmentation-to-biomarker pipeline and identify model miscalibration as the dominant source of uncertainty. Extensive experiments show that our method produces statistically sound confidence intervals, with tunable confidence levels, enabling more trustworthy application of segmentation-derived RBBs in clinical workflows.