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Related Concept Videos

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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
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Residual Stresses01:26

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Residual stresses reside in a structure even after removing the original stress inducer. This phenomenon often arises from varied plastic deformations across different parts of a structure. Consider a rod stretched beyond its yield point. It will not regain its original length due to permanent deformation. Even after load removal, the rod does not entirely lose stress because of uneven plastic deformations, resulting in residual stresses. The computation of these stresses in structures is...
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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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In materials that exhibit elastic and plastic behavior, known as elastoplastic materials, residual stresses can accumulate when these materials experience plastic deformation. This deformation arises from either high levels of shearing stress or significant strains. Residual stresses are internal stresses that persist within a material after removing the external force causing deformation. This phenomenon is demonstrated when observing the behavior of a shaft under torque; notably, the...
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Classification and Quantification of Emphysema Using a Multi-Scale Residual Network.

Liying Peng, Lanfen Lin, Hongjie Hu

    IEEE Journal of Biomedical and Health Informatics
    |January 4, 2019
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    This study introduces a novel multi-scale residual network for automated emphysema classification from CT scans. The new method improves accuracy and identifies a more precise measure of emphysema severity correlated with lung function.

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    Area of Science:

    • Medical Imaging
    • Pulmonary Medicine
    • Artificial Intelligence

    Background:

    • Automated tissue classification is crucial for emphysema analysis and treatment.
    • Existing methods face challenges with inter-class variations (different tissue scales) and intra-class variations (image intensity differences).

    Purpose of the Study:

    • To develop a novel deep learning approach for accurate automated tissue classification in emphysema.
    • To address inter- and intra-class variations in CT images for improved emphysema analysis.
    • To establish a more accurate quantitative measure of emphysema severity linked to pulmonary function.

    Main Methods:

    • A multi-scale residual network utilizing both raw CT images and their differential excitation components.
    • Incorporation of multi-scale information to handle variations in emphysematous tissue appearance.
    • Using a dual-channel input strategy to manage intensity variations across different scans.

    Main Results:

    • Achieved a superior classification accuracy of 93.74% on an original emphysema database.
    • Demonstrated strong correlations between specific emphysema subtypes (CLE, PLE) and pulmonary functions (|r| = 0.922).
    • Proposed a new measure (sum of CLE and PLE) as a more accurate indicator of emphysema severity.

    Conclusions:

    • The proposed multi-scale residual network effectively addresses challenges in automated emphysema classification.
    • The novel quantitative measure combining centrilobular and panlobular emphysema shows significant clinical relevance for assessing disease severity.
    • This approach offers a promising tool for improved diagnosis and management of emphysema.