Combining Classifiers using their Receiver Operating Characteristics and Maximum Likelihood Estimation.

Citation:

Haker S, Wells WM, Warfield SK, Talos I-F, Bhagwat JG, Goldberg-Zimring D, Mian A, Ohno-Machado L, Zou KH. Combining Classifiers using their Receiver Operating Characteristics and Maximum Likelihood Estimation. Med Image Comput Comput Assist Interv. 2005;8 (Pt 1) :506-14. Copy at http://www.tinyurl.com/y4y8ca24

Date Published:

2005

Abstract:

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.

Last updated on 10/07/2016