Towards Reliable WMH Segmentation under Domain Shift: An Application Study using Maximum Entropy Regularization to Improve Uncertainty Estimation

• URL: http://arxiv.org/abs/2506.14497v1
• Date: Tue, 17 Jun 2025 13:21:29 GMT
• Title: Towards Reliable WMH Segmentation under Domain Shift: An Application Study using Maximum Entropy Regularization to Improve Uncertainty Estimation
• Authors: Franco Matzkin, Agostina Larrazabal, Diego H Milone, Jose Dolz, Enzo Ferrante,
• Abstract summary: Accurate segmentation of white matter hyperintensities (WMH) is crucial for clinical decision-making.<n> domain shifts, such as variations in MRI machine types or acquisition parameters, pose challenges to model calibration and uncertainty estimation.<n>This study proposes maximum-entropy regularization techniques to enhance model calibration and uncertainty estimation.
• Score: 9.854116809395896
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate segmentation of white matter hyperintensities (WMH) is crucial for clinical decision-making, particularly in the context of multiple sclerosis. However, domain shifts, such as variations in MRI machine types or acquisition parameters, pose significant challenges to model calibration and uncertainty estimation. This study investigates the impact of domain shift on WMH segmentation by proposing maximum-entropy regularization techniques to enhance model calibration and uncertainty estimation, with the purpose of identifying errors post-deployment using predictive uncertainty as a proxy measure that does not require ground-truth labels. To do this, we conducted experiments using a U-Net architecture to evaluate these regularization schemes on two publicly available datasets, assessing performance with the Dice coefficient, expected calibration error, and entropy-based uncertainty estimates. Our results show that entropy-based uncertainty estimates can anticipate segmentation errors, and that maximum-entropy regularization further strengthens the correlation between uncertainty and segmentation performance while also improving model calibration under domain shift.

Related papers

• CLUE: Neural Networks Calibration via Learning Uncertainty-Error alignment [7.702016079410588]
  We introduce CLUE (Calibration via Learning Uncertainty-Error Alignment), a novel approach that aligns predicted uncertainty with observed error during training.<n>We show that CLUE achieves superior calibration quality and competitive predictive performance with respect to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-05-28T19:23:47Z)
• Wasserstein Distributionally Robust Nonparametric Regression [9.65010022854885]
  This paper studies the generalization properties of Wasserstein distributionally robust nonparametric estimators.<n>We establish non-asymptotic error bounds for the excess local worst-case risk.<n>The robustness of the proposed estimator is evaluated through simulation studies and illustrated with an application to the MNIST dataset.
  arXiv  Detail & Related papers  (2025-05-12T18:07:37Z)
• Uncertainty Weighted Gradients for Model Calibration [22.39558434131574]
  Deep networks often produce over-confident or under-confident predictions, leading to miscalibration.<n>We propose a unified loss framework for focal loss and its variants, where we mainly attribute their superiority in model calibration to the loss weighting factor.<n>Our method achieves state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2025-03-26T04:16:05Z)
• Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials [0.0]
  Uncertainty estimations for machine learning interatomic potentials (MLIPs) are crucial for quantifying model error.<n>We evaluate uncertainty estimations of GPR-based MLIPs, including the predictive GPR standard deviation and ensemble-based uncertainties.
  arXiv  Detail & Related papers  (2024-10-27T10:06:09Z)
• Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation? [26.336947440529713]
  We investigate the impact of data augmentation on confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks. We show that data augmentation improves calibration and uncertainty in cross-genre and cross-lingual setting. We also show that increasing the size of the augmentation further improves calibration and uncertainty.
  arXiv  Detail & Related papers  (2024-07-02T08:49:43Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Which Invariance Should We Transfer? A Causal Minimax Learning Approach [18.71316951734806]
  We present a comprehensive minimax analysis from a causal perspective. We propose an efficient algorithm to search for the subset with minimal worst-case risk. The effectiveness and efficiency of our methods are demonstrated on synthetic data and the diagnosis of Alzheimer's disease.
  arXiv  Detail & Related papers  (2021-07-05T09:07:29Z)
• Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction [63.202627467245584]
  We introduce a Bayesian variational framework to quantify the model-immanent (epistemic) uncertainty. We demonstrate that our approach yields competitive results for undersampled MRI reconstruction.
  arXiv  Detail & Related papers  (2021-02-12T18:08:14Z)
• Unsupervised Calibration under Covariate Shift [92.02278658443166]
  We introduce the problem of calibration under domain shift and propose an importance sampling based approach to address it. We evaluate and discuss the efficacy of our method on both real-world datasets and synthetic datasets.
  arXiv  Detail & Related papers  (2020-06-29T21:50:07Z)
• 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)
• GenDICE: Generalized Offline Estimation of Stationary Values [108.17309783125398]
  We show that effective estimation can still be achieved in important applications. Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions. The resulting algorithm, GenDICE, is straightforward and effective.
  arXiv  Detail & Related papers  (2020-02-21T00:27:52Z)
• Learning to Predict Error for MRI Reconstruction [67.76632988696943]
  We demonstrate that predictive uncertainty estimated by the current methods does not highly correlate with prediction error. We propose a novel method that estimates the target labels and magnitude of the prediction error in two steps.
  arXiv  Detail & Related papers  (2020-02-13T15:55:32Z)

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.