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ICDM
2007
IEEE

Predictive Data Mining for Lung Nodule Interpretation

13 years 10 months ago
Predictive Data Mining for Lung Nodule Interpretation
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 newly available data from the Lung Image Database Consortium (LIDC). Using data mining, we found promising mappings to clinically relevant, human-interpretable nodule characteristics such as malignancy, margin, spiculation, subtlety, and texture. Bridging the semantic gap between computed image features and radiologist defined diagnostic criteria allows CAD systems to offer not only a second opinion but also decision-support criteria usable by radiologists. Presenting transparent decisions will improve ...
William Horsthemke, Ekarin Varutbangkul, Daniela S
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICDM
Authors William Horsthemke, Ekarin Varutbangkul, Daniela Stan Raicu, Jacob D. Furst
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