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Proceedings Paper

A ROC-based feature selection method for computer-aided detection and diagnosis
Author(s): Songyuan Wang; Guopeng Zhang; Qimei Liao; Junying Zhang; Chun Jiao; Hongbing Lu
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Paper Abstract

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.

Paper Details

Date Published: 24 March 2014
PDF: 8 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903505 (24 March 2014); doi: 10.1117/12.2044003
Show Author Affiliations
Songyuan Wang, Fourth Military Medical Univ. (China)
Xidian Univ. (China)
Guopeng Zhang, Fourth Military Medical Univ. (China)
Qimei Liao, Fourth Military Medical Univ. (China)
Junying Zhang, Xidian Univ. (China)
Chun Jiao, Fourth Military Medical Univ. (China)
Hongbing Lu, Fourth Military Medical Univ. (China)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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