CARE: Confidence-aware Ratio Estimation for Medical Biomarkers

Jiameng Li, Teodora Popordanoska, Aleksei Tiulpin, Sebastian G. Gruber, Frederik Maes, Matthew B. Blaschko

Keywords: Medical Imaging Analysis, Uncertainty Quantification, Trustworthy AI

TL;DR: This paper provides adaptive and tunable confidence intervals for ratio-based biomarkers, based on pretrained segmentation network.

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 \textit{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.

Primary Subject Area: Uncertainty Estimation

Secondary Subject Area: Safe and Trustworthy Learning-assisted Solutions for Medical Imaging

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Submission Number: 14