Classification in karyometry: performance testing and prediction error.
Year: 2013
Type: article
Abstract: Classification plays a central role in quantitative histopathology. Success is expressed in terms of the accuracy of prediction for the classification of future data points and an estimate of the prediction error. The prediction error is affected by the chosen procedure, e.g., the use of a training set of data points, a validation set, an independent test set, the sample size and the learning curve of the classification algorithm. For small samples procedures such as the "jackknife," the "leave one out" and the "bootstrap" are recommended in order to arrive at an unbiased estimate of the true prediction error. All of the procedures rest on the assumption that the data set used to derive a classification rule is representative for the diagnostic categories involved. It is this assumption that in quantitative histopathology has to be carefully verified before a clinically generally valid classification procedure can be claimed.
Source: PubMed
Authors: Peter H Bartels, Hubert G Bartels
Institutions: University of Arizona, University of Arizona Cancer Center
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Cancer Institute
OpenAlex ID: W2184096365