Abstract
In this paper we present a cascade-based framework to detect clusters of microcalcifications on mammograms. The algorithm is based on a sliding window technique where a detector is structured as a “cascade” of simple boosting classifiers with increasing complexity. Such a method couples the effectiveness of the cascade approach with the RankBoost algorithm that is aimed at maximizing the area under the ROC curve and represents a good choice when dealing with unbalanced data sets.
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Bria, A., Marrocco, C., Molinara, M., Tortorella, F. (2012). Detecting Clusters of Microcalcifications with a Cascade-Based Approach. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds) Breast Imaging. IWDM 2012. Lecture Notes in Computer Science, vol 7361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31271-7_15
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DOI: https://doi.org/10.1007/978-3-642-31271-7_15
Publisher Name: Springer, Berlin, Heidelberg
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