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Related Experiment Video

Updated: Jun 13, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Comparison of two statistical methodologies for a binary classification problem of two-dimensional images.

Deniz A Sanchez S1, Rubén D Guevara G1, Sergio A Calderón V1

  • 1Facultad de Ciencias, Departamento de Estadística, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia.

Journal of Applied Statistics
|September 13, 2024
PubMed
Summary

This study compares tensor-based and functional data analysis classification methods for image data. Functional data analysis demonstrated superior performance, as measured by the area under the ROC curve (AUC).

Keywords:
62R10Multidimensional arrayclassificationfunctional regressiongeneralized scalar-on-image regressiontensor regressiontotal variation

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Area of Science:

  • Statistical classification
  • Medical image analysis
  • Functional data analysis

Background:

  • Statistical classification methods are crucial for analyzing complex data, including medical images.
  • High-dimensional data, such as images, present unique challenges for traditional statistical models.

Purpose of the Study:

  • To compare the performance of two statistical classification methods: a tensor-based model and a functional data analysis model.
  • To evaluate these methods using images as covariates and the Receiver Operating Characteristic (ROC) curve as the primary criterion.

Main Methods:

  • Implemented a tensor-based classification method within the framework of high-dimensional generalized linear models.
  • Applied a functional data analysis methodology operating in the space of functions with finite total variation.
  • Conducted a simulation study to compare the two methods based on the area under the ROC curve (AUC).

Main Results:

  • The functional data analysis model exhibited better classification performance compared to the tensor-based model.
  • The area under the ROC curve (AUC) was used as the quantitative measure for comparison.

Conclusions:

  • Functional data analysis offers a more effective approach for statistical classification using medical images than the tensor-based model.
  • The findings support the utility of functional data analysis in medical image analysis applications.