TY - GEN
T1 - Probabilistic framework for reliability analysis of information-theoretic CAD systems in mammography
AU - Habas, Piotr A.
AU - Zurada, Jacek M.
AU - Elmaghraby, Adel S.
AU - Tourassi, Georgia D.
PY - 2006
Y1 - 2006
N2 - The purpose of this study is to develop and evaluate a probabilistic framework for reliability analysis of information-theoretic computer-assisted detection (IT-CAD) systems in mammography. The study builds upon our previous work on a feature-based reliability analysis technique tailored to traditional CAD systems developed with a supervised learning scheme. The present study proposes a probabilistic framework to facilitate application of the reliability analysis technique for knowledge-based CAD systems that are not feature-based. The study was based on an information-theoretic CAD system developed for detection of masses in screening mammograms from the Digital Database for Screening Mammography (DDSM). The experimental results reveal that the query-specific reliability estimate provided by the proposed probabilistic framework is an accurate predictor of CAD performance for the query case. It can also be successfully applied as a base for stratification of CAD predictions into clinically meaningful reliability groups (i.e., HIGH, MEDIUM, and LOW). Based on a leave-one-out sampling scheme and ROC analysis, the study demonstrated that the diagnostic performance of the IT-CAD is significantly higher for cases with HIGH reliability (Az = 0.92±0.03) than for those stratified as MEDIUM (Az = 0.84±0.02) or LOW reliability predictions (Az = 0.78±0.02).
AB - The purpose of this study is to develop and evaluate a probabilistic framework for reliability analysis of information-theoretic computer-assisted detection (IT-CAD) systems in mammography. The study builds upon our previous work on a feature-based reliability analysis technique tailored to traditional CAD systems developed with a supervised learning scheme. The present study proposes a probabilistic framework to facilitate application of the reliability analysis technique for knowledge-based CAD systems that are not feature-based. The study was based on an information-theoretic CAD system developed for detection of masses in screening mammograms from the Digital Database for Screening Mammography (DDSM). The experimental results reveal that the query-specific reliability estimate provided by the proposed probabilistic framework is an accurate predictor of CAD performance for the query case. It can also be successfully applied as a base for stratification of CAD predictions into clinically meaningful reliability groups (i.e., HIGH, MEDIUM, and LOW). Based on a leave-one-out sampling scheme and ROC analysis, the study demonstrated that the diagnostic performance of the IT-CAD is significantly higher for cases with HIGH reliability (Az = 0.92±0.03) than for those stratified as MEDIUM (Az = 0.84±0.02) or LOW reliability predictions (Az = 0.78±0.02).
UR - https://www.scopus.com/pages/publications/34047176008
U2 - 10.1109/IEMBS.2006.260500
DO - 10.1109/IEMBS.2006.260500
M3 - Conference contribution
C2 - 17946741
AN - SCOPUS:34047176008
SN - 1424400325
SN - 9781424400324
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 6113
EP - 6116
BT - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
T2 - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Y2 - 30 August 2006 through 3 September 2006
ER -