Confidence Matters: Uncertainty Quantification and Precision Assessment of Deep Learning-based CMR Biomarker Estimates Using Scan-rescan Data

• URL: http://arxiv.org/abs/2603.26789v1
• Date: Wed, 25 Mar 2026 14:45:32 GMT
• Title: Confidence Matters: Uncertainty Quantification and Precision Assessment of Deep Learning-based CMR Biomarker Estimates Using Scan-rescan Data
• Abstract summary: Uncertainty estimation techniques are applied to a state-of-the-art DL pipeline for cardiac functional biomarker estimation.<n>The model achieved high accuracy (average Dice 87%) and point estimate precision on two external validation scan-rescan CMR datasets.
• Score: 4.703422315567864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The performance of deep learning (DL) methods for the analysis of cine cardiovascular magnetic resonance (CMR) is typically assessed in terms of accuracy, overlooking precision. In this work, uncertainty estimation techniques, namely deep ensemble, test-time augmentation, and Monte Carlo dropout, are applied to a state-of-the-art DL pipeline for cardiac functional biomarker estimation, and new distribution-based metrics are proposed for the assessment of biomarker precision. The model achieved high accuracy (average Dice 87%) and point estimate precision on two external validation scan-rescan CMR datasets. However, distribution-based metrics showed that the overlap between scan/rescan confidence intervals was >50% in less than 45% of the cases. Statistical similarity tests between scan and rescan biomarkers also resulted in significant differences for over 65% of the cases. We conclude that, while point estimate metrics might suggest good performance, distributional analyses reveal lower precision, highlighting the need to use more representative metrics to assess scan-rescan agreement.

Related papers

• Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping [0.504092226811181]
  Evaluation on synthetic test data assessed error-awareness, high-error detection, selective prediction, and Gaussian interval calibration.<n> Diffusion model-derived uncertainty was positively associated with the mapping error.<n>Raw uncertainty was poorly calibrated for quantitative interval interpretation.
  arXiv  Detail & Related papers  (2026-08-12T11:30:31Z)
• Beyond Scalar Scores: Exploring LLM-based Metrics for Clinical Significance Evaluation in Radiology Reports [49.5225801722164]
  Existing metrics obscure this requirement by reducing report quality to a medically ungrounded scalar.<n>We study this boundary using ReEvalMed benchmark as testbed and evaluate metric-level clinical significance.
  arXiv  Detail & Related papers  (2026-06-17T08:10:30Z)
• Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis [42.60508892284938]
  We present a large-scale, multi-center benchmark of deep unsupervised anomaly detection for brain imaging.<n>We tested 2,221 T1w and 1,262 T2w scans spanning healthy datasets and diverse clinical cohorts.<n>Our benchmark establishes a transparent foundation for future research and highlights priorities for clinical translation.
  arXiv  Detail & Related papers  (2025-12-01T11:03:27Z)
• Multi-pathology Chest X-ray Classification with Rejection Mechanisms [36.0596663889937]
  Overconfidence in deep learning models poses a significant risk in high-stakes medical imaging tasks.<n>This study introduces an uncertainty-aware framework for chest X-ray diagnosis based on a DenseNet-121 backbone.
  arXiv  Detail & Related papers  (2025-09-12T15:36:26Z)
• Implicit Neural Representations of Intramyocardial Motion and Strain [5.982605533590515]
  We propose a method to predict continuous left ventricular (LV) displacement without requiring inference-time optimisation.<n> Evaluated on 452 UK Biobank test cases, our method achieved the best tracking accuracy (2.14 mm RMSE) and the lowest combined error in global circumferential (2.86%) and radial (6.42%) strain.
  arXiv  Detail & Related papers  (2025-09-10T21:05:27Z)
• A Comprehensive Framework for Uncertainty Quantification of Voxel-wise Supervised Models in IVIM MRI [33.7054351451505]
  We propose a probabilistic deep learning framework based on Deep Ensembles of Mixture Density Networks (MDNs)<n>MDNs produced more calibrated and sharper predictive distributions for the diffusion coefficient D and fraction f parameters, although slight overconfidence was observed in pseudo-diffusion coefficient D*.<n>We present a comprehensive framework for IVIM fitting with uncertainty quantification, which enables the identification and interpretation of unreliable estimates.
  arXiv  Detail & Related papers  (2025-08-06T16:08:55Z)
• Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images [0.795142520168573]
  This study aimed to validate an improved deep learning model for volumetric MSK segmentation of the hip and thigh. The model has shown an overall improvement with respect to all segmentation accuracy and structure volume/density evaluation metrics.
  arXiv  Detail & Related papers  (2024-09-04T14:49:35Z)
• Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation [1.794594355220496]
  Quantification of cardiac biomarkers from cardiovascular magnetic resonance (CMR) data using deep learning (DL) methods offers many advantages. However, only a few studies have focused on the scan-rescan precision of the biomarker estimates. Here, we propose a cardiac biomarker estimation pipeline that not only focuses on achieving high segmentation accuracy but also on improving the scan-rescan precision.
  arXiv  Detail & Related papers  (2024-08-21T16:24:27Z)
• Uncertainty Modeling in Ultrasound Image Segmentation for Precise Fetal Biometric Measurements [0.0]
  This paper delves into the complexities of uncertainty in the segmentation process, focusing on fetal head and femur ultrasound images. The proposed methodology involves extracting target contours and exploring techniques for precise parameter measurement. Uncertainty modeling methods are employed to enhance the training and testing processes of the segmentation network.
  arXiv  Detail & Related papers  (2024-01-17T23:21:42Z)
• 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)
• 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)
• Integrating uncertainty in deep neural networks for MRI based stroke analysis [0.0]
  We present a Bayesian Convolutional Neural Network (CNN) yielding a probability for a stroke lesion on 2D Magnetic Resonance (MR) images. In a cohort of 511 patients, our CNN achieved an accuracy of 95.33% at the image-level representing a significant improvement of 2% over a non-Bayesian counterpart.
  arXiv  Detail & Related papers  (2020-08-13T09:50:17Z)

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.