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Related Concept Videos

Residual Plots01:07

Residual Plots

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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.
When the residual values are plotted against the variable x, it is called a residual...
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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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Residual Stresses in Circular Shafts01:10

Residual Stresses in Circular Shafts

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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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Residual Stresses in Bending01:18

Residual Stresses in Bending

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In the study of elastoplastic members subjected to bending moments, understanding the loading and unloading phases is crucial for assessing material behavior and structural integrity. During the loading phase, as the bending moment increases, the material initially responds elastically, adhering to Hooke's Law, where stress is directly proportional to strain. When the load exceeds the yield strength, plastic deformation occurs, resulting in permanent strain and deformation that remains even...
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Residuals and Least-Squares Property01:11

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Attention Residual Learning for Skin Lesion Classification.

Jianpeng Zhang, Yutong Xie, Yong Xia

    IEEE Transactions on Medical Imaging
    |January 23, 2019
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    This study introduces an attention residual learning convolutional neural network (ARL-CNN) for improved skin lesion classification. The novel model enhances diagnostic accuracy by adaptively focusing on key lesion features in dermoscopy images.

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    Area of Science:

    • Dermatology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Automated skin lesion classification is crucial for early melanoma detection and reducing mortality.
    • Deep convolutional neural networks (DCNNs) face challenges in skin lesion analysis due to limited data, similar lesion appearances, and difficulty focusing on relevant areas.

    Purpose of the Study:

    • To develop an advanced deep learning model for accurate skin lesion classification using dermoscopy images.
    • To address limitations of existing DCNNs, including insufficient data and poor focus on discriminative lesion features.

    Main Methods:

    • Proposed an attention residual learning convolutional neural network (ARL-CNN) model.
    • Integrated residual learning with a novel attention mechanism that leverages self-attention within DCNNs.
    • Utilized feature maps from higher layers to generate attention maps for lower layers, enhancing feature representation without additional learnable layers.

    Main Results:

    • The ARL-CNN model demonstrated the ability to adaptively focus on discriminative parts of skin lesions.
    • Achieved state-of-the-art performance in skin lesion classification on the ISIC-skin 2017 dataset.

    Conclusions:

    • The proposed ARL-CNN model effectively improves skin lesion classification accuracy.
    • The attention mechanism enhances the model's ability to identify critical features, offering a promising approach for clinical diagnosis.