Dev P. Chakraborty

Observer Performance Methods for Diagnostic Imaging (eBook, PDF)

Foundations, Modeling, and Applications with R-Based Examples

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 590
• Erscheinungstermin: 14. Dezember 2017
• Englisch
• ISBN-13: 9781482214864
• Artikelnr.: 57480406

Autorenporträt

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.

Inhaltsangabe

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