Combining classifiers using their receiver operating characteristics and maximum likelihood estimation

Authors:
Steven Haker;William M. Wells;Simon K. Warfield;Ion-Florin Talos;Jui G. Bhagwat;Daniel Goldberg-Zimring;Asim Mian;Lucila Ohno-Machado;Kelly H. Zou
Affiliations:
Surgical Planning Lab, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA;Surgical Planning Lab, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA;Surgical Planning Lab, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA;Surgical Planning Lab, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA;Surgical Planning Lab, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA;Surgical Planning Lab, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA;Surgical Planning Lab, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA;Surgical Planning Lab, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA;Surgical Planning Lab, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

Citing 2
Cited 3

On Combining Classifiers

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The use of receiver operating characteristic curves in biomedical informatics

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Iterative Boolean combination of classifiers in the ROC space: An application to anomaly detection with HMMs

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Fusion of biometric systems using Boolean combination: an application to iris-based authentication

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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.