Improved model for specimen classification based on single-cell classifiers

We consider probabilistic models for specimen classification procedures based on systems which classify individual cells as normal or abnormal. The models which we consider generalize those discussed previously by Castleman and White (Anal. Quant. Cytol. 2:117-122, 1980; Cytometry 2:155-158, 1981) and by Timmers and Gelsema (Cytometry 6:22-25, 1985). In particular, they include the biologically plausible possibility that the specimen contains cells which are intermediate between the extremes of normal and abnormal. We find that if these additional cells occur differentially in normal and abnormal specimens, then specimen classification can become substantially more efficient when the cell classifier has different error rates for these cells.