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Related Concept Videos

Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Class-specific weighting for Markov random field estimation: application to medical image segmentation.

James P Monaco1, Anant Madabhushi

  • 1Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ, USA. jpmonaco@rci.rutgers.edu

Medical Image Analysis
|September 19, 2012
PubMed
Summary

This study introduces multiplicative weighted maximum a posteriori (MWMAP) and maximum posterior marginals (MWMPM) estimation for Bayesian classifiers. These methods allow adjustable classifier performance in Markov random fields, enhancing accuracy in medical image analysis.

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Area of Science:

  • Computer Science, Artificial Intelligence
  • Medical Imaging
  • Statistics, Probability

Background:

  • Bayesian classifiers require adjustable performance (e.g., sensitivity/specificity) for estimation tasks.
  • Existing methods for adjusting classifier performance are not compatible with Markov random fields (MRFs).
  • MRF-based classification systems are limited to static operating points, hindering performance flexibility.

Purpose of the Study:

  • To introduce a novel strategy for adjusting the performance of maximum a posteriori (MAP) estimators in MRFs.
  • To develop multiplicative weighted MAP (MWMAP) and multiplicative weighted maximum posterior marginals (MWMPM) estimation criteria.
  • To demonstrate the application and flexibility of MWMAP and MWMPM in various classification tasks.

Main Methods:

  • Incorporation of multiplicative weights into the MAP cost function to create MWMAP estimation.
  • Application of the multiplicative weighting strategy to the MPM cost function, yielding MWMPM estimation.
  • Implementation of MWMAP and MWMPM using adaptations of iterated conditional modes and MPM Monte Carlo.

Main Results:

  • MWMAP and MWMPM provide a means for adjusting classifier performance by creating a natural bias for specific classes.
  • Demonstrated successful integration of MWMAP and MWMPM into MRF-based systems for prostate cancer detection in histological sections and MRI.
  • Showcased the extensibility of MWMAP and MWMPM to multi-class segmentation tasks, such as synthetic brain MR image segmentation.

Conclusions:

  • MWMAP and MWMPM enable arbitrary adjustment of classifier sensitivities, generating receiver operator characteristic curves and surfaces.
  • The proposed methods address the lack of adjustable performance in MRF-based classification systems.
  • MWMAP and MWMPM offer a valuable tool for enhancing the flexibility and applicability of MRF-based classifiers in diverse estimation tasks.