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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Gradient and Del Operator01:14

Gradient and Del Operator

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In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

259
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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Updated: May 28, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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EAMAPG:通过预测梯度下降进行可解释的对抗模型分析.

Ahmad Chaddad1, Yuchen Jiang2, Tareef S Daqqaq3

  • 1Artificial Intelligence for Personalized Medicine, School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, 541004, China; Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, H3C 1K3, Canada.

Computers in biology and medicine
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PubMed
概括
此摘要是机器生成的。

本研究介绍了使用预测梯度下降 (PGD) 的对抗生成,以提高医疗图像分析中的深度学习 (DL) 模型的可解释性. 该方法确定了影响DL决策的关键特征,提高了放射科医生的透明度.

关键词:
深度学习是一种深度学习.可解释的人工智能预计的梯度下降.

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科学领域:

  • 医学图像分析 医学图像分析
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 深度学习 (DL) 模型在医学图像分析中表现出高性能,但缺乏透明度.
  • 对DL模型的解释性对于临床采用和信任至关重要.

研究的目的:

  • 开发和评估一种新的方法,以提高医疗图像分析中DL模型的可解释性.
  • 使用对抗性示例识别影响DL模型决策的关键图像特征.

主要方法:

  • 利用预测梯度下降 (PGD) 通过引入导致错误分类的扰动来生成对抗性示例.
  • 应用对抗生成方法来分析来自脑瘤,眼病和COVID-19数据集的医学图像.
  • 评估了六种常见的卷积神经网络 (CNN) 模型,重点关注像DenseNet121,InceptionV3和ResNet101这样的高性能模型.

主要成果:

  • 对抗性干扰显著增加了模型损失 (p < 0.05),证实了成功的对抗性生成,并突出了模型的漏洞.
  • 在各种医学成像数据集 (脑瘤,眼睛疾病,COVID-19) 中证明了该方法的有效性.
  • 在选择的模型中实现了高性能指标:DenseNet121 (AUC 1.00),InceptionV3 (AUC 0.99) 和ResNet101 (AUC 1.00).

结论:

  • 拟议的对抗生成方法提供了一种新的方法,用于改善医疗成像中DL模型的解释性.
  • 这种技术提供了对DL模型决策的更直观的理解,弥合了AI能力和临床应用之间的差距.
  • 这些发现支持可解释AI在临床环境中的实际使用,帮助放射科医生做出决策.