# Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomography

arXiv: https://arxiv.org/abs/2503.00136 · HTML: https://mlsanity.com/paper/2503.00136/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Conformal Risk Control](https://mlsanity.com/paper/2208.02814/) (2022)
- [Distribution-Free, Risk-Controlling Prediction Sets](https://mlsanity.com/paper/2101.02703/) (2021)
- [Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging](https://mlsanity.com/paper/2202.05265/) (2022)
- [AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks](https://mlsanity.com/paper/2305.09666/) (2023)
- [How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control](https://mlsanity.com/paper/2302.03791/) (2023)
- [Length Optimization in Conformal Prediction](https://mlsanity.com/paper/2406.18814/) (2024)
- [Hallucination Index: An Image Quality Metric for Generative Reconstruction Models](https://mlsanity.com/paper/2407.12780/) (2024)
- [Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty](https://mlsanity.com/paper/2405.05145/) (2024)
- [Uncertainty Quantification via Neural Posterior Principal Components](https://mlsanity.com/paper/2309.15533/) (2023)
- [Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms](https://mlsanity.com/paper/2311.11837/) (2023)
- [On Volume Minimization in Conformal Regression](https://mlsanity.com/paper/2502.09985/) (2025)
- [Conformal Performance Range Prediction for Segmentation Output Quality Control](https://mlsanity.com/paper/2407.13307/) (2024)
- [Conformal confidence sets for biomedical image segmentation](https://mlsanity.com/paper/2410.03406/) (2024)
- [Conformal Risk Control for Pulmonary Nodule Detection](https://mlsanity.com/paper/2412.20167/) (2024)

## Built on by (most cited first, 8 of 8)

- [Conditional Conformal Risk Adaptation](https://mlsanity.com/paper/2504.07611/) (2025)
- [CHEM: Estimating and Understanding Hallucinations in Deep Learning for Image Processing](https://mlsanity.com/paper/2512.09806/) (2025)
- [QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems](https://mlsanity.com/paper/2507.14760/) (2025)
- [Trustworthy Endoscopic Super-Resolution](https://mlsanity.com/paper/2604.18001/) (2026)
- [When Average Calibration Fails: Site-Conditional Federated Conformal Risk Control](https://mlsanity.com/paper/2606.20115/) (2026)
- [Bound-Aware Per-Organ Recall Risk Control for Multi-Organ CT Segmentation under Clinical Domain Shift](https://mlsanity.com/paper/2608.18193/) (2026)
- [CRC-Router: Risk-Constrained Routing for Medical Agentic AI Systems](https://mlsanity.com/paper/2609.30714/) (2026)
- [Uncertainty as a Proxy for Semantic Correctness in Diffusion-Based Medical Image Synthesis](https://mlsanity.com/paper/2610.03224/) (2026)

## Abstract (arXiv)

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.