Cascaded Rank-Based Classifiers for Detecting Clusters of Microcalcifications

Alessandro, Bria; Claudio, Marrocco; Mario, Molinara; Francesco, Tortorella

doi:10.1007/978-3-642-38326-7_25

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A Computer Aided Detection (CAD) system has frequently to deal with a significant skew between positive and negative class. For this reason we propose a solution based on an ensemble of classifiers structured as a “cascade” of dichotomizers where each node is robust to such skew since it is trained by a learning algorithm based on ranking instead of classification error. The proposed approach has been applied to the detection of clusters of microcalcifications in mammograms and has shown good performance in comparison with other methods well suited to deal with unbalanced problems.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11580/36857

Titolo:	Cascaded Rank-Based Classifiers for Detecting Clusters of Microcalcifications
Autori interni:	BRIA, Alessandro MARROCCO, Claudio MOLINARA, Mario TORTORELLA, Francesco
Data di pubblicazione:	2013
Serie:	LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
Abstract:	A Computer Aided Detection (CAD) system has frequently to deal with a significant skew between positive and negative class. For this reason we propose a solution based on an ensemble of classifiers structured as a “cascade” of dichotomizers where each node is robust to such skew since it is trained by a learning algorithm based on ranking instead of classification error. The proposed approach has been applied to the detection of clusters of microcalcifications in mammograms and has shown good performance in comparison with other methods well suited to deal with unbalanced problems.
Handle:	http://hdl.handle.net/11580/36857
ISBN:	9783642383250 9783642383267
Appare nelle tipologie:	2.1 Contributo in volume (Capitolo o Saggio)

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