Anatomically-aware conformal prediction for medical image segmentation with random walks

• URL: http://arxiv.org/abs/2601.18997v1
• Date: Mon, 26 Jan 2026 22:16:07 GMT
• Title: Anatomically-aware conformal prediction for medical image segmentation with random walks
• Authors: Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert,
• Abstract summary: Conformal prediction (CP) is a powerful distribution-free framework for constructing statistically valid prediction intervals.<n>This paper proposes Random-Walk Conformal Prediction (RW-CP), a model-agnostic framework which can be added on top of any segmentation method.
• Score: 8.829058131683764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The reliable deployment of deep learning in medical imaging requires uncertainty quantification that provides rigorous error guarantees while remaining anatomically meaningful. Conformal prediction (CP) is a powerful distribution-free framework for constructing statistically valid prediction intervals. However, standard applications in segmentation often ignore anatomical context, resulting in fragmented, spatially incoherent, and over-segmented prediction sets that limit clinical utility. To bridge this gap, this paper proposes Random-Walk Conformal Prediction (RW-CP), a model-agnostic framework which can be added on top of any segmentation method. RW-CP enforces spatial coherence to generate anatomically valid sets. Our method constructs a k-nearest neighbour graph from pre-trained vision foundation model features and applies a random walk to diffuse uncertainty. The random walk diffusion regularizes the non-conformity scores, making the prediction sets less sensitive to the conformal calibration parameter $λ$, ensuring more stable and continuous anatomical boundaries. RW-CP maintains rigorous marginal coverage while significantly improving segmentation quality. Evaluations on multi-modal public datasets show improvements of up to $35.4\%$ compared to standard CP baselines, given an allowable error rate of $α=0.1$.

Related papers

• LATA: Laplacian-Assisted Transductive Adaptation for Conformal Uncertainty in Medical VLMs [61.06744611795341]
  Medical vision-language models (VLMs) are strong zero-shot recognizers for medical imaging.<n>We propose texttttextbfLATA (Laplacian-Assisted Transductive Adaptation), a textittraining- and label-free refinement.<n>texttttextbfLATA sharpens zero-shot predictions without compromising exchangeability.
  arXiv  Detail & Related papers  (2026-02-19T16:45:38Z)
• Controlling False Positives in Image Segmentation via Conformal Prediction [2.339515934428971]
  We introduce a simple framework that constructs confidence masks with distribution-free, image-level control of false-positive predictions.<n>Our framework enables practical, risk-aware segmentation in settings where over-segmentation can have clinical consequences.
  arXiv  Detail & Related papers  (2025-11-19T13:02:50Z)
• COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metrics [23.593891761167374]
  Conformal prediction (CP) is a popular framework to derive principled uncertainty guarantees, but it treats the complex segmentation-to-metric pipeline as a black box.<n>We introduce a framework that generates efficient, metric-based CP intervals for image segmentation models by leveraging the inductive biases of their underlying deep neural networks.
  arXiv  Detail & Related papers  (2025-09-26T11:56:28Z)
• FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimation [4.544160712377809]
  This work presents a novel federated learning approach to achieve universal segmentation across diverse abdominal CT datasets.<n>The proposed method quantifies prediction uncertainty by propagating the uncertainty from the model weights.<n> Experimental evaluations demonstrate the effectiveness of this approach in improving the quality of federated aggregation and uncertainty-weighted inference.
  arXiv  Detail & Related papers  (2025-08-08T11:34:01Z)
• COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
  COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
  arXiv  Detail & Related papers  (2025-06-25T07:04:49Z)
• Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal prediction [2.7320409129940684]
  Current uncertainty quantification approaches often fall short, particularly when combined with normality assumptions.<n>We present two novel approaches guaranteeing finite-sample validity for multi-output regression problems.<n>Our methods consistently outperform existing multi-output conformal prediction approaches in both validity and efficiency.
  arXiv  Detail & Related papers  (2025-03-18T10:21:32Z)
• Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression [47.1405366895538]
  Sparse linear regression methods for high-dimensional data commonly assume that residuals have constant variance, which can be violated in practice. This paper proposes estimating high-dimensional heteroscedastic linear regression models using a heteroscedastic partitioned empirical Bayes Expectation Conditional Maximization algorithm.
  arXiv  Detail & Related papers  (2023-09-15T22:06:29Z)
• Towards Better Certified Segmentation via Diffusion Models [62.21617614504225]
  segmentation models can be vulnerable to adversarial perturbations, which hinders their use in critical-decision systems like healthcare or autonomous driving. Recently, randomized smoothing has been proposed to certify segmentation predictions by adding Gaussian noise to the input to obtain theoretical guarantees. In this paper, we address the problem of certifying segmentation prediction using a combination of randomized smoothing and diffusion models.
  arXiv  Detail & Related papers  (2023-06-16T16:30:39Z)
• Uncertainty Quantification over Graph with Conformalized Graph Neural Networks [52.20904874696597]
  Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. GNNs lack rigorous uncertainty estimates, limiting their reliable deployment in settings where the cost of errors is significant. We propose conformalized GNN (CF-GNN), extending conformal prediction (CP) to graph-based models for guaranteed uncertainty estimates.
  arXiv  Detail & Related papers  (2023-05-23T21:38:23Z)
• Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty [57.023423137202485]
  Concerns regarding the reliability of medical image segmentation persist among clinicians.<n>We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks.<n>By leveraging subjective logic theory, we explicitly model probability and uncertainty for medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets [0.7734726150561088]
  A lack of statistically rigorous uncertainty quantification is a significant factor undermining trust in AI results. Recent developments in distribution-free uncertainty quantification present practical solutions for these issues. We demonstrate a technique for forming ordinal prediction sets that are guaranteed to contain the correct stenosis severity.
  arXiv  Detail & Related papers  (2022-07-05T18:01:20Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.