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24 March 2014 A ROC-based feature selection method for computer-aided detection and diagnosis
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Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier’s suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.
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Songyuan Wang, Guopeng Zhang, Qimei Liao, Junying Zhang, Chun Jiao, and Hongbing Lu "A ROC-based feature selection method for computer-aided detection and diagnosis", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903505 (24 March 2014);

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