在标签有噪声的医学影像回归中，提出高效可靠的置信区间生成方法。
Efficient Conformal Prediction for Regression Models under Label Noise
- 基于校准集噪声建模，推导出无噪声置信阈值的数学估计方法
- 在两个含高斯噪声的医学影像数据集上，性能接近干净标签场景
- 适用于医疗等高风险领域，对噪声鲁棒性强
在医疗影像等高风险场景中，为回归模型的预测提供可靠的置信区间至关重要。近年来，分位数预测（Conformal Prediction, CP）作为一种强大的统计框架，基于带标签的校准集，可生成以预设概率包含真实标签的区间。本文针对校准集中存在标签噪声时如何应用CP的问题，首先建立了一种数学上严谨的无噪声CP阈值估计方法；随后将其转化为克服回归问题连续性挑战的实际算法。我们在两个含高斯标签噪声的医学影像回归数据集上评估了该方法，结果表明其显著优于现有方法，性能接近于无噪声标签设置。
原文摘要 · Abstract (English)
In high-stakes scenarios, such as medical imaging applications, it is critical to equip the predictions of a regression model with reliable confidence intervals. Recently, Conformal Prediction (CP) has emerged as a powerful statistical framework that, based on a labeled calibration set, generates intervals that include the true labels with a pre-specified probability. In this paper, we address the problem of applying CP for regression models when the calibration set contains noisy labels. We begin by establishing a mathematically grounded procedure for estimating the noise-free CP threshold. Then, we turn it into a practical algorithm that overcomes the challenges arising from the continuous nature of the regression problem. We evaluate the proposed method on two medical imaging regression datasets with Gaussian label noise. Our method significantly outperforms the existing alternative, achieving performance close to the clean-label setting.
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