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Diagnostic decision-making in pulmonary medical imaging has been improved by computer-aided diagnosis (CAD) systems, serving as second readers to detect suspicious nodules for diagnosis by a radiologist. Though increasing accurate, these CAD systems rarely offer useful descriptions of the suspected nodule or their decision criteria, mainly due to lack of nodule data. In this paper, we present a framework for mapping image features to radiologist-defined diagnostic criteria based on the newlydoi:10.1109/icdmw.2007.23 dblp:conf/icdm/HorsthemkeVRF07 fatcat:l3efso2u2nae7dary4grdzixna