mlsanityData to 14 Oct 2026

CARE: Confidence-aware Ratio Estimation for Medical Biomarkers

arXiv 2505.19585 ↗First posted 26 May 2025cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Ratio-based biomarkers (RBBs), such as the proportion of necrotic tissue within a tumor, are widely used in clinical practice to support diagnosis, prognosis, and treatment planning. These biomarkers are typically estimated from segmentation outputs by computing region-wise ratios. Despite the high-stakes nature of clinical decision making, existing methods provide only point estimates, offering no measure of uncertainty. In this work, we propose a unified confidence-aware framework for estimating ratio-based biomarkers. Our uncertainty analysis stems from two observations: (1) the probability ratio estimator inherently admits a statistical confidence interval regarding local randomness (bias and variance); (2) the segmentation network is not perfectly calibrated (calibration error).We perform a systematic analysis of error propagation in the segmentation-to-biomarker pipeline and identify model miscalibration as the dominant source of uncertainty. Extensive experiments show that our method produces statistically sound confidence intervals, with tunable confidence levels, enabling more trustworthy application of segmentation-derived RBBs in clinical workflows.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 52 references that are in the map. First posted on arXiv 26 May 2025.

Builds on

25 of 52 references.

References · 25

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2017On Calibration of Modern Neural Networkscited by 4,960
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  4. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  5. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  6. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
  7. 2019Conformalized Quantile Regressioncited by 629
  8. 2021The Medical Segmentation Decathloncited by 628
  9. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
  10. 2019Verified Uncertainty Calibrationcited by 309
  11. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 273
  12. 2020Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learningcited by 190
13 more
  1. 2019Evaluating model calibration in classificationcited by 168
  2. 2019Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentationcited by 163
  3. 2022Better Uncertainty Calibration via Proper Scores for Classification and Beyondcited by 65
  4. 2022A Consistent and Differentiable Lp Canonical Calibration Error Estimatorcited by 47
  5. 2020Transferable Calibration with Lower Bias and Variance in Domain Adaptationcited by 41
  6. 2022Personalizing Federated Medical Image Segmentation via Local Calibrationcited by 29
  7. 2021Precise characterization of the prior predictive distribution of deep ReLU networkscited by 28
  8. 2022On Calibrating Semantic Segmentation Models: Analyses and An Algorithmcited by 25
  9. 2023Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challengecited by 19
  10. 2023Quantifying Deep Learning Model Uncertainty in Conformal Predictioncited by 13
  11. 2020Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessmentcited by 12
  12. 2021On the relationship between calibrated predictors and unbiased volume estimationcited by 11
  13. 2019Bayesian Feature Pyramid Networks for Automatic Multi-Label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratiocited by 2

Built on by

Not cited yet.