Abstract
Computer aided detection systems for mammography typically use standard classification algorithms from machine learning for detecting lesions. However, these general purpose learning algorithms make implicit assumptions that are commonly violated in CAD problems. We propose a new ensemble algorithm that explicitly accounts for the small fraction of outlier images which tend to produce a large number of false positives. A bootstrapping procedure is used to ensure that the candidates from these outlier images do not skew the statistical properties of the training samples. Experimental studies on the detection of clusters of micro-calcifications indicate that the proposed method significantly outperforms a state-of-the-art general purpose method for designing classifiers (SVM), in terms of FROC curves on a hold out test set.
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© 2006 Springer-Verlag Berlin Heidelberg
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Fung, G. et al. (2006). Addressing Image Variability While Learning Classifiers for Detecting Clusters of Micro-calcifications. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_12
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DOI: https://doi.org/10.1007/11783237_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35625-7
Online ISBN: 978-3-540-35627-1
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