ConRad：用于放射组学的高效共形预测
ConRad: Efficient Conformal Prediction for Radiomics
AI总结：
研究针对放射组学中分割模型问题，提出ConRad共形框架，利用预测掩码等派生协变量构建自适应区间，在五个二维医学成像数据集及171个放射组学目标上实验，提高了特征级效率且保持覆盖率，消融结果显示分割边界不确定性特征贡献大。
摘要：
从医学图像和分割掩码中提取的放射组学特征用于支持临床成像流程中的决策。实际上，这些特征常根据预测掩码计算，但分割模型可能过度自信或校准不佳，使派生测量看起来比实际更可靠。共形预测（CP）提供无分布预测区间并保证有限样本边际覆盖率，但用于分割派生放射组学的黑箱区间效率低下，因为忽略了图像外观、掩码几何形状和分割不确定性的测试时间信息。我们提出ConRad，一种用于标量放射组学目标的共形框架，它使用从预测掩码、输入图像、预测放射组学和边界不确定性派生的协变量来构建自适应区间，同时保持覆盖率。在五个二维医学成像数据集和171个保留的放射组学目标上，我们表明ConRad与基线相比提高了特征级效率，同时保持接近名义的经验覆盖率。消融结果进一步表明，分割边界不确定性特征对区间效率贡献最大。
英文摘要：
Radiomic features derived from medical images and segmentation masks are used to support decision making in clinical imaging pipelines. In practice, these features are often computed from predicted masks, but segmentation models can be overconfident or poorly calibrated, making derived measurements appear more reliable than they are. Conformal prediction (CP) provides distribution-free prediction intervals with finite-sample marginal coverage guarantees, but black-box intervals for segmentation-derived radiomics can be inefficient because they ignore test-time information about image appearance, mask geometry, and segmentation uncertainty. We propose ConRad, a conformal framework for scalar radiomic targets that uses covariates derived from the predicted mask, input image, predicted radiomics, and boundary uncertainty to construct adaptive intervals while maintaining coverage. Across five 2D medical imaging datasets and 171 retained radiomic targets, we show that ConRad improves feature-level efficiency compared to baselines while maintaining near-nominal empirical coverage. Ablation results further indicate that segmentation boundary uncertainty features are the largest contributors to interval efficiency.
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