为医学比率型生物标志物提供带置信度的估计，提升临床决策可信度。
CARE: Confidence-aware Ratio Estimation for Medical Biomarkers
- 基于分割结果计算比率时，引入统计置信区间来量化不确定性。
- 实验证明可生成符合统计理论的置信区间，且置信水平可调。
- 特别适合对可靠性要求高的医疗影像分析场景。
比率型生物标志物（RBBs），如肿瘤内坏死组织比例，在临床诊断、预后评估和治疗方案制定中广泛应用。这些标志物通常通过分割结果计算区域比率得到。然而，现有方法仅提供点估计，缺乏不确定性度量。本文提出统一的置信度感知框架，用于估计比率型生物标志物。不确定性分析基于两点：（1）概率比估计器本身在局部随机性（偏差与方差）下具有统计置信区间；（2）分割网络存在校准误差。我们系统分析了从分割到生物标志物的误差传播过程，发现模型校准误差是不确定性的主要来源。大量实验表明，所提方法可生成统计上可靠的置信区间，且置信水平可调，使分割衍生的RBBs在临床流程中更具可信度。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。