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The Empirical Variance Estimator for Computer Aided Diagnosis: Lessons for Algorithm Validation

  • Alex F. Mendelson
  • Maria A. Zuluaga
  • Lennart Thurfjell
  • Brian F. Hutton
  • Sébastien Ourselin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Computer aided diagnosis is an established field in medical image analysis; a great deal of effort goes into the development and refinement of pipelines to achieve greater performance. This improvement is dependent on reliable comparison, which is intimately related to variance estimation. For supervised methods, this can be confounded by statistical issues at the comparatively small sample sizes typical of the field. Given the importance of reliable comparison to pipeline development, this issue has received relatively little attention. As a solution, we advocate an empirical variance estimator based on validation within disjoint subsets of the available data. Using Alzheimer’s disease classification in the ADNI dataset as an examplar, we investigate the behaviour of different variance estimators in a series of resampling experiments. We show that the proposed estimator is unbiased, and that it exceeds the estimates of naive approaches, which are biased down. Because the estimator avoids independence assumptions, it is able to accommodate arbitrary validation strategies and performance metrics. As it is unbiased, it is able to provide statistically convincing comparison and confidence intervals for algorithm performance. Finally, we show how the estimator can be used to compare different validation strategies, and make some recommendations about which should be used.

Keywords

Cross Validation Variance Estimator Unbiased Estimator Validation Strategy Medical Image Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alex F. Mendelson
    • 1
  • Maria A. Zuluaga
    • 1
  • Lennart Thurfjell
    • 2
  • Brian F. Hutton
    • 3
    • 4
  • Sébastien Ourselin
    • 1
    • 5
  1. 1.Translational Imaging Group, Centre for Medical Image ComputingUniversity College LondonLondonUK
  2. 2.GE HealthcareUppsalaSweden
  3. 3.Institute of Nuclear MedicineUniversity College LondonLondonUK
  4. 4.Centre for Medical Radiation PhysicsUniversity of WollongongAustralia
  5. 5.Dementia Research CentreUniversity College LondonUK

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