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相关概念视频

Normal Distribution01:11

Normal Distribution

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The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is...
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Normal Stress01:19

Normal Stress

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Normal stress is a type of stress that occurs when forces act perpendicular, or normal, to a material's cross-sectional area. This stress often arises in structures when subjected to axial loading, which is the application of force along the axis of an object. A practical example of this can be found in bridge truss members.
When a rod is under axial loading, the internal forces and corresponding stress are normal to the plane of the section, so it is termed normal stress. It's important to...
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Introduction to Normal Distributions01:29

Introduction to Normal Distributions

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Standardized test scores often follow a symmetric distribution that can be modeled with the normal distribution, a fundamental concept in statistics. This distribution is particularly useful for interpreting test performance fairly across populations, as it provides a mathematical framework for understanding variability and central tendency in large datasets.From Histogram to Frequency DistributionRaw test data are often displayed using histograms, where the height of each bar represents the...
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Applications of Normal Distribution01:22

Applications of Normal Distribution

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The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.
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Normal and Shear Force01:14

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When a beam is subjected to different loads, such as weight, pressure, or other external forces, internal forces are generated within the beam. These forces can have a significant impact on the overall stability and strength of the structure. Engineers use various methods to analyze and determine the magnitude and direction of these internal forces. One common technique used to determine internal forces in beams is the method of sections. This method involves considering an imaginary point or...
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Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System
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正常乳腺组织 (NBT) 分类器:在正常乳腺组织学中推进分区分类.

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  • 1Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.

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人工智能 (AI) 模型,NBT-分类器,现在可以从整个幻灯片图像中分析正常的乳腺组织 (NBT). 这些模型准确地识别组织区,有助于早期发现和预防乳腺癌.

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科学领域:

  • 计算病理学计算病理学
  • 数字组织病理学 数字组织病理学
  • 人工智能在瘤学中的应用

背景情况:

  • 对正常乳腺组织 (NBT) 的定量分析对于早期癌症检测至关重要,但仍然有限.
  • 使用数字化幻灯片的计算性组织病理学提供了潜力,但缺乏基于AI的NBT分析.

研究的目的:

  • 开发和验证AI模型用于正常乳腺组织的分类.
  • 为NBT区间分析建立强大的分析工具,以帮助早期发现乳腺癌.

主要方法:

  • 精选了70张全片图像 (WSI) 的NBT与病理学家指导的注释.
  • 开发基于卷积神经网络 (CNN) 的补丁级分类模型 (NBT-分类器).
  • 在三个外部队列中使用不同的补丁大小 (128x128μm和256x256μm) 验证了模型.

主要成果:

  • 在外部队列中,NBT-分类器实现了高性能,AUC为0.98-1.00.
  • 这些模型确定了正常组织的独特特征,将其与癌前和癌性表皮区分开来.
  • 可解释的人工智能技术可视化了学习的特征,并将其集成到管道中,促进了周叶球区域分析.

结论:

  • NBT-分类器提供正常乳腺组织的准确,隔间特异性分析.
  • 这些工具增强了对NBT形态学的理解,作为识别癌前变化的参考.
  • 开发的模型通过改进的定量分析支持早期乳腺癌预防策略.