Optimizing decision tree structures for spectral histopathology (SHP)

Abstract

This paper reviews methods to arrive at optimum decision tree or label tree structures to analyze large SHP datasets. Supervised methods of analysis can utilize either sequential or (flat) multi-classifiers depending on the variance in the data, and on the number of spectral classes to be distinguished. For small number of spectral classes, multi-classifiers have been used in the past, but for the analysis of datasets containing large numbers (∼20) of disease or tissue types, mixed decision tree structures were found to be advantageous. In these mixed structures, discrimination into classes and subclasses is achieved via hierarchical decision/label tree structures.