title: Probabilistic framework for reliability analysis of information-theoretic CAD systems in mammography.
abstract: 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 (A(z) = 0.92 +/- 0.03) than for those stratified as MEDIUM (A(z) = 0.84 +/- 0.02) or LOW reliability predictions (A(z) = 0.78 +/- 0.02).
authors.affiliation: Computational Intelligence Lab., Louisville Univ., KY 40292, USA. p.habas@ieee.org
authors.forename: Piotr A
authors.initials: PA
authors.lastname: Habas
authors.forename: Jacek M
authors.initials: JM
authors.lastname: Zurada
authors.forename: Adel S
authors.initials: AS
authors.lastname: Elmaghraby
authors.forename: Georgia D
authors.initials: GD
authors.lastname: Tourassi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001941:Breast Diseases / Q000175:diagnosis* / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D007256:Information Systems; D008327:Mammography / Q000295:instrumentation* / Q000379:methods; D015233:Models, Statistical; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D011336:Probability; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted