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The tradeoff of cell classifier error rates
Author(s) -
Castleman Kenneth R.,
White Benjamin S.
Publication year - 1980
Publication title -
cytometry
Language(s) - English
Resource type - Journals
eISSN - 1097-0320
pISSN - 0196-4763
DOI - 10.1002/cyto.990010211
Subject(s) - false positive paradox , classifier (uml) , false positives and false negatives , true positive rate , computer science , artificial intelligence , pattern recognition (psychology) , word error rate , statistics , mathematics
In gynecologic cytodiagnosis it is generally agreed that specimen false negative errors are more serious than false positives. When classifying individual cells, however, it is not obvious how one should adjust the parameters that control the two cell error rates. In the case where a specimen classifier follows a cell classifier, one can calculate the sample size required to achieve specified overall performance. This analysis shows that for the small abnormal cell proportions encountered in cervical cytology, cell false positives should be kept so low that a substantial portion of the abnormal cells are missed.

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