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2007 Frontiers in the Convergence of Bioscience and Information Technologies
Computer-Aided Diagnosis of Cross-Institutional Mammograms Using Support Vector Machines with Feature Elimination
Jeju Island, Korea
October 11-October 13
ISBN: 978-0-7695-2999-8
ASCII Text
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Saejoon Kim, Sejong Yoon, Donghyuk Shin, "Computer-Aided Diagnosis of Cross-Institutional Mammograms Using Support Vector Machines with Feature Elimination," Frontiers in the Convergence of Bioscience and Information Technologies, pp. 396-402, 2007 Frontiers in the Convergence of Bioscience and Information Technologies, 2007.
 
 
BibTex
x
 
@article{ 10.1109/FBIT.2007.9,
author = {Saejoon Kim and Sejong Yoon and Donghyuk Shin},
title = {Computer-Aided Diagnosis of Cross-Institutional Mammograms Using Support Vector Machines with Feature Elimination},
journal ={Frontiers in the Convergence of Bioscience and Information Technologies},
volume = {0},
year = {2007},
isbn = {978-0-7695-2999-8},
pages = {396-402},
doi = {http://doi.ieeecomputersociety.org/10.1109/FBIT.2007.9},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Frontiers in the Convergence of Bioscience and Information Technologies
TI - Computer-Aided Diagnosis of Cross-Institutional Mammograms Using Support Vector Machines with Feature Elimination
SN - 978-0-7695-2999-8
SP396
EP402
A1 - Saejoon Kim,
A1 - Sejong Yoon,
A1 - Donghyuk Shin,
PY - 2007
VL - 0
JA - Frontiers in the Convergence of Bioscience and Information Technologies
ER -
 
In the analysis of digital or digitized mammographic images, a requirement is to learn to separate benign calcifications from malignant ones. Such an activity could form part of a computer-aided diagnosis (CAD) tool. We present a CAD study of calcification lesions to demonstrate that CAD of same-institutional mammograms provides significantly higher accuracy compared to that of cross-institutional mammograms. Moreover, using only a subset of the widely used six BI-RADS features together with patient age and subtlety value describing each calcification lesion is shown to increase the accuracy of CAD.
Citation:
Saejoon Kim, Sejong Yoon, Donghyuk Shin, "Computer-Aided Diagnosis of Cross-Institutional Mammograms Using Support Vector Machines with Feature Elimination," fbit, pp.396-402, 2007 Frontiers in the Convergence of Bioscience and Information Technologies, 2007
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