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Proceedings Paper

False-positive reduction of liver tumor detection using ensemble learning method
Author(s): Atsushi Miyamoto; Junichi Miyakoshi; Kazuki Matsuzaki; Toshiyuki Irie
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Paper Abstract

We proposed a novel ensemble learning method which can be applied to false-positive reduction of liver tumor detection. In many cases of the liver tumor detection, training data has some issues due to characteristics of liver tumors, and the conventional ensemble learning methods such as Bagging and AdaBoost tend to degrade sensitivity. The proposed method generates various weak classifiers based on adaptive sampling in order to enhance an ensemble effect against such issues, and can achieve accuracy satisfying requirements of liver tumor detection. We applied the method to 48 CT images and evaluated the accuracy. Results showed that the proposed method succeeded in reducing false positives greatly (from 3.96 to 1.10/image) while maintaining the required sensitivity.

Paper Details

Date Published: 13 March 2013
PDF: 6 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86693B (13 March 2013); doi: 10.1117/12.2006329
Show Author Affiliations
Atsushi Miyamoto, Hitachi, Ltd. (Japan)
Junichi Miyakoshi, Hitachi, Ltd. (Japan)
Kazuki Matsuzaki, Hitachi, Ltd. (Japan)
Toshiyuki Irie, Hitachi General Hospital, Hitachi, Ltd. (Japan)


Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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