Paper Info
Title
Eliminating false-positive microcalcification clusters in a mammography CAD scheme using a Bayesian neural network
Abstract
We have applied a Bayesian neural network (BNN) to the task of distinguishing between true-positive (TP) and false-positive (FP) detected clusters in a computer-aided diagnosis (CAD) scheme for detecting clustered microcalcifications in mammograms. Because BNNs can approximate ideal observer decision functions given sufficient training data, this approach should have better performance than our previous FP cluster elimination methods. Eight cluster-based features were extracted from the TP and FP clusters detected by the scheme in a training dataset of 39 mammograms. This set of features was used to train a BNN with eight input nodes, five hidden nodes, and one output node. The trained BNN was tested on the TP and Fl? clusters detected by our scheme in an independent testing set of 50 mammograms. The BNN output was analyzed using ROC and FROC analysis. The detection scheme with the BNN for FP cluster elimination had substantially better cluster sensitivity at low Fl? rates (below 0.8 FP dusters per image) than the original detection scheme without the BNN. Our preliminary research shows that a BNN can improve the performance of our scheme for detecting clusters of microcalcifications.
Year
DOI
Venue
2001
10.1117/12.431089
Proceedings of SPIE
Keywords
Field
DocType
CAD,mammography,clustered microcalcifications,Bayesian artificial neural networks,ideal observer approximation
CAD,Data mining,Cluster (physics),Mammography,Microcalcification,Computer science,Computer-aided diagnosis,Bayesian neural networks,Observer (quantum physics),Artificial neural network
Conference
Volume
ISSN
Citations 
4322
0277-786X
1
PageRank 
References 
Authors
0.43
0
5
Name
Order
Citations
PageRank
Darrin C. Edwards1608.89
john papaioannou274.34
Yulei Jiang3808.90
Matthew A Kupinski413626.32
Robert M Nishikawa559958.25