Related papers: Uncertainty Evaluation Metric for Brain Tumour Segmentation

Related papers

Rethinking Uncertainty in Segmentation: From Estimation to Decision [0.0]
In medical image segmentation, uncertainty estimates are often reported but rarely used to guide decisions.<n>We formulate segmentation as a two-stage pipeline, estimation followed by decision, and show that optimizing uncertainty alone fails to capture most of the achievable safety gains.<n>Our results show that the best method and policy combination removes up to 80 percent of segmentation errors at only 25 percent pixel deferral.
arXiv Detail & Related papers (2026-04-14T19:52:05Z)
Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations [49.84786015324238]
Confidence estimation (CE) indicates how reliable the answers of large language models (LLMs) are, and can impact user trust and decision-making.<n>We present a comprehensive evaluation framework for CE that measures their confidence quality on three new aspects.<n>These include robustness of confidence against prompt perturbations, stability across semantic equivalent answers, and sensitivity to semantically different answers.
arXiv Detail & Related papers (2026-01-12T23:16:50Z)
Uncertainty Quantification for Machine Learning: One Size Does Not Fit All [27.02918627964384]
We argue that uncertainty quantification should be tailored to the specific application.<n>In particular, we show that, for the task of selective prediction, the scoring rule should ideally match the task loss.<n>In an active learning setting, epistemic uncertainty based on zero-one loss is shown to consistently outperform other uncertainty measures.
arXiv Detail & Related papers (2025-12-13T14:15:04Z)
Uncertainty Quantification for Regression using Proper Scoring Rules [76.24649098854219]
We introduce a unified UQ framework for regression based on proper scoring rules, such as CRPS, logarithmic, squared error, and quadratic scores.<n>We derive closed-form expressions for the uncertainty measures under practical parametric assumptions and show how to estimate them using ensembles of models.<n>Our broad evaluation on synthetic and real-world regression datasets provides guidance for selecting reliable UQ measures.
arXiv Detail & Related papers (2025-09-30T17:52:12Z)
SConU: Selective Conformal Uncertainty in Large Language Models [59.25881667640868]
We propose a novel approach termed Selective Conformal Uncertainty (SConU) We develop two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level. Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions.
arXiv Detail & Related papers (2025-04-19T03:01:45Z)
On Information-Theoretic Measures of Predictive Uncertainty [5.8034373350518775]
Despite its significance, a consensus on the correct measurement of predictive uncertainty remains elusive. Our proposed framework categorizes predictive uncertainty measures according to two factors: (I) The predicting model (II) The approximation of the true predictive distribution. We empirically evaluate these measures in typical uncertainty estimation settings, such as misclassification detection, selective prediction, and out-of-distribution detection.
arXiv Detail & Related papers (2024-10-14T17:52:18Z)
Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In! [80.3129093617928]
Annually, at the Conference of Machine Translation (WMT), the Metrics Shared Task organizers conduct the meta-evaluation of Machine Translation (MT) metrics. This work highlights two issues with the meta-evaluation framework currently employed in WMT, and assesses their impact on the metrics rankings. We introduce the concept of sentinel metrics, which are designed explicitly to scrutinize the meta-evaluation process's accuracy, robustness, and fairness.
arXiv Detail & Related papers (2024-08-25T13:29:34Z)
Label-wise Aleatoric and Epistemic Uncertainty Quantification [15.642370299038488]
We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. We show that our proposed measures adhere to a number of desirable properties.
arXiv Detail & Related papers (2024-06-04T14:33:23Z)
Uncertainty in Language Models: Assessment through Rank-Calibration [65.10149293133846]
Language Models (LMs) have shown promising performance in natural language generation. It is crucial to correctly quantify their uncertainty in responding to given inputs. We develop a novel and practical framework, termed $Rank$-$Calibration$, to assess uncertainty and confidence measures for LMs.
arXiv Detail & Related papers (2024-04-04T02:31:05Z)
Second-Order Uncertainty Quantification: Variance-Based Measures [2.3999111269325266]
This paper proposes a novel way to use variance-based measures to quantify uncertainty on the basis of second-order distributions in classification problems. A distinctive feature of the measures is the ability to reason about uncertainties on a class-based level, which is useful in situations where nuanced decision-making is required.
arXiv Detail & Related papers (2023-12-30T16:30:52Z)
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)
Uncertainty Estimation for Heatmap-based Landmark Localization [4.673063715963989]
We propose Quantile Binning, a data-driven method to categorise predictions by uncertainty with estimated error bounds. We demonstrate this framework by comparing and contrasting three uncertainty measures. We conclude by illustrating how filtering out gross mispredictions caught in our Quantile Bins significantly improves the proportion of predictions under an acceptable error threshold.
arXiv Detail & Related papers (2022-03-04T14:40:44Z)
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection [78.24964622317634]
In this work, the formulation of Bayesian autoencoders (BAEs) is adopted to quantify the total anomaly uncertainty. To evaluate the quality of uncertainty, we consider the task of classifying anomalies with the additional option of rejecting predictions of high uncertainty. Our experiments demonstrate the effectiveness of the BAE and total anomaly uncertainty on a set of benchmark datasets and two real datasets for manufacturing.
arXiv Detail & Related papers (2022-02-25T12:20:04Z)
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation -- Analysis of Ranking Metrics and Benchmarking Results [29.106087655992862]
Deep learning (DL) models have provided the state-of-theart performance in a wide variety of medical imaging benchmarking challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions.
arXiv Detail & Related papers (2021-12-19T06:45:55Z)
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.