ConRad: Efficient Conformal Prediction for Radiomics

2026-07-08

AI Summary

This study addresses the lack of reliable uncertainty quantification in radiomics features derived from predicted segmentation masks, which often stems from model overconfidence. To this end, the authors propose ConRad, a novel framework that, for the first time, incorporates test-time segmentation boundary uncertainty into conformal prediction. ConRad constructs adaptive prediction intervals by jointly leveraging the input image, the predicted mask, and their geometric and appearance characteristics. Extensive experiments across five 2D medical imaging datasets and 171 radiomics features demonstrate that ConRad significantly outperforms existing baseline methods in interval efficiency while maintaining coverage close to the nominal level.

Abstract

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.

Problem

• radiomics
• conformal prediction
• segmentation uncertainty
• prediction intervals
• medical imaging

Innovation

• Conformal Prediction
• Radiomics
• Segmentation Uncertainty
• Adaptive Prediction Intervals
• Medical Imaging