Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomography
By: Jacopo Teneggi, J Webster Stayman, Jeremias Sulam
Potential Business Impact:
Shows how sure a computer is about medical scans.
Uncertainty quantification is necessary for developers, physicians, and regulatory agencies to build trust in machine learning predictors and improve patient care. Beyond measuring uncertainty, it is crucial to express it in clinically meaningful terms that provide actionable insights. This work introduces a conformal risk control (CRC) procedure for organ-dependent uncertainty estimation, ensuring high-probability coverage of the ground-truth image. We first present a high-dimensional CRC procedure that leverages recent ideas of length minimization. We make this procedure semantically adaptive to each patient's anatomy and positioning of organs. Our method, sem-CRC, provides tighter uncertainty intervals with valid coverage on real-world computed tomography (CT) data while communicating uncertainty with clinically relevant features.
Similar Papers
Conditional Conformal Risk Adaptation
Machine Learning (CS)
Improves medical scans to find problems more reliably.
Selective Conformal Risk Control
Machine Learning (CS)
Makes AI predictions more trustworthy and useful.
Conformal Risk Training: End-to-End Optimization of Conformal Risk Control
Machine Learning (CS)
Guarantees AI predictions are safe and reliable.