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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Computed Tomography01:10

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Related Experiment Video

Updated: May 7, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

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A framework for quantifying and leveraging uncertainty in pre-trained CT denoising model.

Hao Gong, Nathan R Huber, Shravani A Kharat

    IEEE Transactions on Bio-Medical Engineering
    |May 5, 2026
    PubMed
    Summary

    This study introduces a novel framework for quantifying and utilizing total uncertainty in deep learning models for low-dose CT denoising. The framework enhances diagnostic image quality and lesion detectability, improving trustworthiness in AI-driven medical imaging.

    Related Experiment Videos

    Last Updated: May 7, 2026

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    43.7K

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence in Radiology
    • Deep Learning for Medical Image Analysis

    Background:

    • Pre-trained deep learning models offer potential for low-dose computed tomography (CT) denoising.
    • Accurate estimation and utilization of total uncertainty (aleatoric and epistemic) are crucial for reliable AI deployment in medical imaging.
    • Existing models often lack robust uncertainty quantification, limiting their clinical utility and trustworthiness.

    Purpose of the Study:

    • To develop an architecture-agnostic framework for estimating, calibrating, and leveraging total uncertainty in pre-trained deep learning denoising models for low-dose CT.
    • To improve the diagnostic image quality and lesion detectability in low-dose CT scans.
    • To establish a foundation for performance monitoring, deployment optimization, and trustworthiness of AI models in medical imaging.

    Main Methods:

    • Developed a framework estimating aleatoric uncertainty via physics-based inference-time augmentation and epistemic uncertainty using training-free Monte Carlo dropout.
    • Implemented non-parametric re-calibration to enhance uncertainty calibration, followed by adaptive local fusion (ALF) guided by the local mean-to-uncertainty ratio.
    • Validated the framework on U-net and ResNet models across diverse CT datasets, assessing uncertainty with NRMSE/NCE and image quality/lesion detectability using SSIM and a deep learning model observer.

    Main Results:

    • The framework achieved accurate uncertainty quantification and calibration, with NRMSE in [1.2%, 2.4%] and NCE in [0.9%, 2.2%].
    • ALF demonstrated comparable or reduced noise levels compared to original models, with significant improvements in lesion structural fidelity (SSIM) and detectability (p<0.05).
    • Specific improvements included up to 69.7% noise reduction for lung nodules and up to 13.2% enhanced detectability for liver metastases.

    Conclusions:

    • The developed framework effectively quantifies and utilizes total uncertainty to enhance diagnostic image quality in low-dose CT using pre-trained denoising models.
    • This approach facilitates improved performance monitoring, optimized deployment, and increased trustworthiness of AI in medical imaging applications.
    • The architecture-agnostic nature of the framework allows broad applicability across various deep learning denoising models for CT.