Observer Performance Methods for Diagnostic Imaging - Chakraborty, Dev P.
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Avis sur Observer Performance Methods For Diagnostic Imaging Format Relié - Livre Médecine, Pharmacie, Paramédical, Médecine vétérinaire
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Présentation Observer Performance Methods For Diagnostic Imaging Format Relié
- Livre Médecine, Pharmacie, Paramédical, Médecine vétérinaire
Résumé :
This book provides a complete, introductory overview of this growing field and its applications in medical imaging, utilizing worked examples and exercises to demystify statistics for readers of any background. It covers discussion of study design basics and use of the techniques in imaging system optimization, among other topics....
Biographie: Dev P. Chakraborty received his PhD in physics in 1977 from the University of Rochester, NY. Following postdoctoral fellowships at the University of Pennsylvania (UPENN) and the University of Alabama at Birmingham (UAB), since 1982 he has worked as a clinical diagnostic imaging physicist. He is American Board of Radiology certified in Diagnostic Radiological Physics and Medical Nuclear Physics (1987). He has held faculty positions at UAB (1982 - 1988), UPENN (1988-2002) and the University of Pittsburgh (2002-2016). At UPENN he supervised hospital imaging equipment quality control, resident physics instruction and conducted independent research. He is an author on 78 peer-reviewed publications, the majority of which are first-authored. He has received research funding from the Whittaker Foundation, the Office of Women's Health, the FDA, the DOD, and has served as principal investigator on several NIH RO1 grants.
Sommaire: 1 Preliminaries PART A The receiver operating characteristic (ROC) paradigm 2 The binary paradigm 3 Modeling the binary task 4 The ratings paradigm 5 Empirical AUC 6 Binormal model 7 Sources of variability in AUC PART B Two significance testing methods for the ROC paradigm 8 Hypothesis testing 9 Dorfman-Berbaum-Metz-Hillis (DBMH) analysis 10 Obuchowski-Rockette-Hillis (ORH) analysis 11 Sample size estimation PART C The free-response ROC (FROC) paradigm 12 The FROC paradigm 13 Empirical operating characteristics possible with FROC data 14 Computation and meanings of empirical FROC FOM-statistics and AUC measures 15 Visual search paradigms 16 The radiological search model (RSM) 17 Predictions of the RSM 18 Analyzing FROC data 19 Fitting RSM to FROC/ROC data and key findings PART D Selected advanced topics 20 Proper ROC models 21 The bivariate binormal model 22 Evaluating standalone CAD versus radiologists 23 Validating CAD analysis
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