ConRad: Efficient Conformal Prediction for Radiomics
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 scal...
| Authors | Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan |
|---|---|
| Primary category | eess.IV |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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