Uncertainty of Vision Medical Foundation Models

• URL: http://arxiv.org/abs/2608.30390v1
• Date: Mon, 31 Aug 2026 07:43:50 GMT
• Title: Uncertainty of Vision Medical Foundation Models
• Abstract summary: We study impact of pre-training approach, dataset scale and domain on both point and region-level uncertainty quantification.<n>Our results demonstrate that pre-training on higher-quality domain-specific datasets along with self-supervised learning leads to better-calibrated point predictions.
• Score: 1.6402320507315202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate uncertainty estimation is essential for machine learning systems de- ployed in high-stakes domains such as medicine. Traditional approaches primarily rely on probability outputs from trained models (point predictions), which provide no formal guarantees on prediction coverage and often require additional calibra- tion techniques to improve reliability. In contrast, conformal prediction (region prediction) offers a principled alternative by generating prediction sets with finite- sample validity guarantees, ensuring that the ground truth is contained within the set at a specified confidence level. In this study, we explore the impact of pre-training approach, dataset scale and domain on both point and region-level uncertainty quantification, by studying domain-specific vision medical foundation models vs. general domain vision foundation models. We conduct a comprehensive evaluation across foundation models trained on retinal, histopathological, and Chest X-Rays data, applying various calibration techniques. Our results demonstrate that (1) pre-training on higher-quality domain-specific datasets along with self-supervised learning leads to better-calibrated point predictions than general domain pre-training, (2) stan- dard re-calibration methods alone cannot fully mitigate uncertainty discrepancies across models trained on different data sources, (3) domain-specific foundation model can lead to more efficient conformal prediction. These findings highlight the importance of careful model selection and the inte- gration of both point and region prediction to enhance the reliability and trust- worthiness of medical AI systems. Our work underscores the need for a holistic approach to uncertainty quantification in recent development of medical vision foundation model, ensuring robust and interpretable AI-driven decision-making.

Related papers

• Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models [56.29123284262618]
  This work proposes leveraging the universal representation capabilities of visual foundation models to estimate inherent data uncertainty.<n>We analyze the feature diversity of the model's decoded representations and quantify their singular value energy to define the semantic perception scale for each class.<n>Based on this foundation, we design two uncertainty-driven application strategies: (1) the aleatoric uncertainty-aware data filtering mechanism to eliminate potentially noisy samples and enhance model learning quality; and (2) the dynamic uncertainty-aware optimization strategy that adaptively adjusts class-specific loss weights during training based on the semantic perception scale, combined with a label denoising mechanism to improve training stability
  arXiv  Detail & Related papers  (2026-04-13T03:59:54Z)
• Data-Driven Priors for Uncertainty-Aware Deterioration Risk Prediction with Multimodal Data [11.640422721732756]
  Current machine learning models frequently lack reliable uncertainty estimation, hindering real-world deployment.<n>We propose $textttMedCertAIn$, a predictive uncertainty framework that leverages multimodal clinical data for in-hospital risk prediction.<n>We train and evaluate the models with such priors using clinical time-series and chest X-ray images from the publicly-available datasets MIMIC-IV and MIMIC-CXR.
  arXiv  Detail & Related papers  (2026-03-09T14:54:38Z)
• Adaptive-CaRe: Adaptive Causal Regularization for Robust Outcome Prediction [16.391352325575763]
  Supervised machine learning algorithms are commonly used for outcome prediction in the medical domain.<n>We propose a novel model-agnostic regularization strategy, Adaptive-CaRe, for generalized outcome prediction in the medical domain.
  arXiv  Detail & Related papers  (2026-02-06T11:14:03Z)
• Conformal Prediction for Multi-Source Detection on a Network [59.17729745907474]
  We study the multi-source detection problem.<n>Given snapshot observations of node infection status on a graph, estimate the set of source nodes that initiated the propagation.<n>We propose a novel conformal prediction framework that provides statistically valid recall guarantees for source set detection.
  arXiv  Detail & Related papers  (2025-11-12T01:09:56Z)
• A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis [1.6690512882610855]
  Deep learning models can be used to continuously monitor physiological parameters outside of clinical settings.<n>There is risk of poor performance when deployed in practical measurement scenarios leading to negative patient outcomes.<n>Here we implement eight uncertainty (UQ) techniques to models trained on two clinically relevant prediction tasks.
  arXiv  Detail & Related papers  (2025-10-31T22:54:13Z)
• Uncertainty Awareness on Unsupervised Domain Adaptation for Time Series Data [49.36938105983916]
  Unsupervised domain adaptation methods seek to generalize effectively on unlabeled test data.<n>We propose incorporating multi-scale feature extraction and uncertainty estimation to improve the model's generalization and robustness across domains.
  arXiv  Detail & Related papers  (2025-08-26T03:13:08Z)
• Beyond Uncertainty Quantification: Learning Uncertainty for Trust-Informed Neural Network Decisions - A Case Study in COVID-19 Classification [7.383605511698832]
  Reliable uncertainty quantification is critical in high-stakes applications, such as medical diagnosis.<n>Traditional uncertainty quantification methods rely on a predefined confidence threshold to classify predictions as confident or uncertain.<n>This approach assumes that predictions exceeding the threshold are trustworthy, while those below it are uncertain, without explicitly assessing the correctness of high-confidence predictions.<n>This study proposes an uncertainty-aware stacked neural network, which extends conventional uncertainty quantification by learning when predictions should be trusted.
  arXiv  Detail & Related papers  (2024-09-19T04:20:12Z)
• 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)
• Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction [9.199022926064009]
  The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. We present a method to provide people with clinical expertise with domain-relevant evidence about why a prediction should be trusted.
  arXiv  Detail & Related papers  (2021-03-04T00:26:32Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• 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.