Optimality of a utility-based performance metric for ROC analysis

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 6917, Issue , 2008, Pages

  Edwards, Darrin C ^a   Metz, Charles E ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

Expected utility; Ideal observer estimation; ROC methodology

Indexed keywords

(1 1 0) SURFACE; (SPM) CLASSES; AREA UNDER THE ROC CURVE (AUC); CLASSIFICATION TASKS; DATA MODELLING; DECISION VARIABLE (DV); EXPECTED COSTS; IDEAL OBSERVERS; IMAGE PERCEPTION; LIKELIHOOD RATIO (LR); NUMERICAL STUDIES; OBSERVER PERFORMANCES; OPTIMAL VALUES; OPTIMALITY; PERFORMANCE METRIC; PROPERTY (S); RECEIVER OPERATING CHARACTERISTIC (ROC) ANALYSIS; RECEIVER OPERATING CHARACTERISTIC (ROC) CURVES; TECHNOLOGY ASSESSMENT (TA);

CLASSIFICATION (OF INFORMATION); COMPUTER NETWORKS; CURVES (ROAD); ELECTRIC NETWORK ANALYSIS; HIGHWAY BRIDGES; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; MEDICAL IMAGING; MICROFLUIDICS; MULTITASKING; NUMERICAL ANALYSIS; TECHNOLOGICAL FORECASTING; TECHNOLOGY; VARIATIONAL TECHNIQUES;

IMAGING TECHNIQUES;

EID: 45249085023     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.773052     Document Type: Conference Paper

References (9)

1. J. P. Egan, Signal Detection Theory and ROC Analysis, Academic Press, New York, 1975.
2. C. E. Metz, "Basic principles of ROC analysis," Seminars in Nuclear Medicine VIII(4), pp. 283-298, 1978.
3. D. C. Edwards, L. Lan, C. E. Metz, M. L. Giger, and R. M. Nishikawa, "Estimating three-class ideal observer decision variables for computerized detection and classification of mammographic mass lesions," Med. Phys. 31, pp. 81-90, 2004.
4. H. L. Van Trees, Detection, Estimation and Modulation Theory: Part I, John Wiley & Sons, New York, 1968.
5. D. C. Edwards, C. E. Metz, and M. A. Kupinski, "Ideal observers and optimal ROC hypersurfaces in N-class classification," IEEE Trans. Med. Irnag. 23, pp. 891-895, 2004.
6. C. E. Metz and X. Pan, "'Proper' binomial ROC curves: Theory and maximum-likelihood estimation," J. Math. Psychol. 43, pp. 1-33, 1999.
7. D. C. Edwards, C. E. Metz, and R. M. Nishikawa, "The hypervolume under the ROC hypersurface of 'near-guessing' and 'near-perfect' observers in N-class classification tasks," IEEE Trans. Med. Imag. 24, pp. 293-299, 2005.
8. D. C. Edwards and C. E. Metz, "A utility-based performance metric for ROC analysis of N-class classification tasks," in Proc. SPIE Vol. 6515 Medical Imaging 2007: Image Perception. Observer Performance, and Technology Assessment, Yulei Jiang and Berkman Sahiner, eds., pp. 6515031-65150310, (SPIE, Bellingham, WA), 2007.
9. C. E. Metz, B. A. Herman, and J.-H. Shen, "Maximum-likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data," Statist. Med. 17, pp. 1033-1053, 1998.