Ideal observer approximation using Bayesian classification neural networks.

Ideal observer approximation using Bayesian classification neural networks.

It is well understood that the optimal classification decision variable is the likelihood ratio or any monotonic transformation of the likelihood ratio. An automated classifier which maps from an input space to one of the likelihood ratio family of decision variables is an optimal classifier or &quo...

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Publié dans:IEEE Transactions on medical imaging 20, 9 (2001).
Auteur principal: Kupinski, M.A
Format: Article
Langue:English
Sujets:
Bayesian classification neural networks.
Artificial neural networks.
Automated classifier.
Computer-aided diagnosis.
Data complexity.
Ideal observer approximation.
Ideal observer decision variables.
Large training sample sizes.
Likelihood ratio.
Medical diagnostic imaging.
Monotonic transformation.
Optimal classifier.
Optimal mapping function.