mlsanityData to 14 Oct 2026

Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomography

arXiv 2503.00136 ↗First posted 28 Feb 2025cs.CV stat.MLcited by 8

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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.

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

18 of 33 references.

References · 18

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  3. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 613
  4. 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
  5. 2022Conformal Risk Controlcited by 299
  6. 2021Distribution-Free, Risk-Controlling Prediction Setscited by 288
  7. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
  8. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  9. 2023How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Controlcited by 50
  10. 2024Length Optimization in Conformal Predictioncited by 39
  11. 2024Hallucination Index: An Image Quality Metric for Generative Reconstruction Modelscited by 26
  12. 2024Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertaintycited by 20
6 more
  1. 2023Uncertainty Quantification via Neural Posterior Principal Componentscited by 16
  2. 2023Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithmscited by 14
  3. 2025On Volume Minimization in Conformal Regressioncited by 9
  4. 2024Conformal Performance Range Prediction for Segmentation Output Quality Controlcited by 8
  5. 2024Conformal confidence sets for biomedical image segmentationcited by 6
  6. 2024Conformal Risk Control for Pulmonary Nodule Detectioncited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025Conditional Conformal Risk Adaptationcited by 7
  2. 2025CHEM: Estimating and Understanding Hallucinations in Deep Learning for Image Processingcited by 1
  3. 2025QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problemscited by 0
  4. 2026Trustworthy Endoscopic Super-Resolutioncited by 0
  5. 2026When Average Calibration Fails: Site-Conditional Federated Conformal Risk Controlcited by 0
  6. 2026Bound-Aware Per-Organ Recall Risk Control for Multi-Organ CT Segmentation under Clinical Domain Shiftcited by 0
  7. 2026CRC-Router: Risk-Constrained Routing for Medical Agentic AI Systemscited by 0
  8. 2026Uncertainty as a Proxy for Semantic Correctness in Diffusion-Based Medical Image Synthesiscited by 0