Maria V. Sainz de Cea

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Due to variability among different subjects, the detection accuracy of microcalcifications (MC) in mammograms often varies greatly from case to case. Even for a well-developed MC detector, its performance can be hampered by a number of factors ranging from imaging noise to inhomogeneity in the breast tissue. To address this issue, we use a Bayes' risk(More)
In computer-aided diagnosis of clustered microcalcifications (MCs), the individual MCs in a lesion need to be first detected prior to subsequent classification as being benign or malignant. However, owing to noise characteristics and patient variability, the detection accuracy is often adversely compromised by the occurrence of false-positives (FPs) or(More)
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