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Related Concept Videos

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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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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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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Normal breast tissue (NBT)-classifiers: advancing compartment classification in normal breast histology.

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Artificial intelligence (AI) models, NBT-Classifiers, can now analyze normal breast tissue (NBT) from whole slide images. These models accurately identify tissue compartments, aiding early breast cancer detection and prevention.

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Area of Science:

  • Computational pathology
  • Digital histopathology
  • Artificial intelligence in oncology

Background:

  • Quantitative analysis of normal breast tissue (NBT) is crucial for early cancer detection but remains limited.
  • Computational histopathology using digitized slides offers potential but lacks AI-based analysis for NBT.

Purpose of the Study:

  • To develop and validate AI models for the classification of normal breast tissue.
  • To establish robust analytical tools for NBT compartment analysis to aid in early breast cancer detection.

Main Methods:

  • Curated 70 whole slide images (WSIs) of NBT with pathologist-guided annotations.
  • Developed convolutional neural network (CNN)-based, patch-level classification models (NBT-Classifiers).
  • Validated models across three external cohorts using different patch sizes (128x128µm and 256x256µm).

Main Results:

  • NBT-Classifiers achieved high performance with AUCs of 0.98-1.00 across external cohorts.
  • The models identified distinct features of normal tissue, differentiating them from precancerous and cancerous epithelium.
  • Explainable AI techniques visualized learned features, and integration into a pipeline facilitated peri-lobular region analysis.

Conclusions:

  • NBT-Classifiers provide accurate, compartment-specific analysis of normal breast tissue.
  • These tools enhance understanding of NBT morphology, serving as references for identifying premalignant changes.
  • The developed models support early breast cancer prevention strategies through improved quantitative analysis.