[2509.22240] COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metrics

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2509.22240 (eess) [Submitted on 26 Sep 2025 (v1), last revised 28 Feb 2026 (this version, v2)] Title:COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metrics Authors:Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan View a PDF of the paper titled COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metrics, by Matt Y. Cheung and 2 other authors View PDF HTML (experimental) Abstract:In clinical applications, the utility of segmentation models is often based on the accuracy of derived downstream metrics such as organ size, rather than by the pixel-level accuracy of the segmentation masks themselves. Thus, uncertainty quantification for such metrics is crucial for decision-making. Conformal prediction (CP) is a popular framework to derive such principled uncertainty guarantees, but applying CP naively to the final scalar metric is inefficient because it treats the complex, non-linear segmentation-to-metric pipeline as a black box. We introduce COMPASS, a practical framework that generates efficient, metric-based CP intervals for image segmentation models by leveraging the inductive biases of their underlying deep neural networks. COMPASS performs calibration directly in the model's representation space by perturbing intermediate features along low-dimensional subspaces maximally sensitive to the target metric. We prove that COMPASS achieves valid marginal c...