Issue 24, 2018

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.

Graphical abstract: Optimizing decision tree structures for spectral histopathology (SHP)

Article information

Article type
Paper
Submitted
15 Шіл. 2018
Accepted
12 Қаз. 2018
First published
12 Қаз. 2018

Analyst, 2018,143, 5935-5939

Optimizing decision tree structures for spectral histopathology (SHP)

X. Mu, S. Remiszewski, M. Kon, A. Ergin and M. Diem, Analyst, 2018, 143, 5935 DOI: 10.1039/C8AN01303A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements