Darrin C Edwards
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Explore the profile of Darrin C Edwards including associated specialties, affiliations and a list of published articles.
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9
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115
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Recent Articles
1.
Edwards D
Acad Radiol
. 2013 Jun;
20(7):908-14.
PMID: 23747155
Rationale And Objectives: Traditional two-class receiver operating characteristic (ROC) analysis is inadequate for the complete evaluation of observer performance in tasks with more than two classes. Materials And Methods: Here,...
2.
Edwards D, Metz C
J Math Psychol
. 2012 Nov;
56(4):256-273.
PMID: 23162165
Although a fully general extension of ROC analysis to classification tasks with more than two classes has yet to be developed, the potential benefits to be gained from a practical...
3.
MacDermed D, Khodarev N, Pitroda S, Edwards D, Pelizzari C, Huang L, et al.
BMC Med Genomics
. 2010 May;
3:16.
PMID: 20459602
Background: MUC1 protein is highly expressed in lung cancer. The cytoplasmic domain of MUC1 (MUC1-CD) induces tumorigenesis and resistance to DNA-damaging agents. We characterized MUC1-CD-induced transcriptional changes and examined their...
4.
Edwards D, Metz C
IEEE Trans Med Imaging
. 2007 Oct;
26(10):1345-56.
PMID: 17948725
We have shown previously that an N-class ideal observer achieves the optimal receiver operating characteristic (ROC) hypersurface in a Neyman-Pearson sense. Due to the inherent complexity of evaluating observer performance...
5.
Edwards D, Metz C
IEEE Trans Med Imaging
. 2005 Dec;
24(12):1566-73.
PMID: 16350917
We are attempting to develop expressions for the coordinates of points on the three-class ideal observer's receiver operating characteristic (ROC) hypersurface as functions of the set of decision criteria used...
6.
Edwards D, Metz C, Nishikawa R
IEEE Trans Med Imaging
. 2005 Mar;
24(3):293-9.
PMID: 15754980
We express the performance of the N-class "guessing" observer in terms of the N2-N conditional probabilities which make up an N-class receiver operating characteristic (ROC) space, in a formulation in...
7.
Edwards D, Metz C, Kupinski M
IEEE Trans Med Imaging
. 2004 Jul;
23(7):891-5.
PMID: 15250641
The likelihood ratio, or ideal observer, decision rule is known to be optimal for two-class classification tasks in the sense that it maximizes expected utility (or, equivalently, minimizes the Bayes...
8.
Edwards D, Lan L, Metz C, Giger M, Nishikawa R
Med Phys
. 2004 Feb;
31(1):81-90.
PMID: 14761024
We are using Bayesian artificial neural networks (BANNs) to classify mammographic masses in schemes for computer-aided diagnosis, and we are extending this methodology to a three-class classification task. We investigated...
9.
Edwards D, Kupinski M, Metz C, Nishikawa R
Med Phys
. 2003 Jan;
29(12):2861-70.
PMID: 12512721
We have developed a model for FROC curve fitting that relates the observer's FROC performance not to the ROC performance that would be obtained if the observer's responses were scored...