C
Craig K. Abbey
Researcher at University of California, Santa Barbara
Publications - 238
Citations - 4738
Craig K. Abbey is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Observer (quantum physics) & Imaging phantom. The author has an hindex of 37, co-authored 218 publications receiving 4407 citations. Previous affiliations of Craig K. Abbey include University of California, San Francisco & University of Arizona.
Papers
More filters
Journal ArticleDOI
Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner.
TL;DR: An experimental paradigm to systematically study the dynamics of perceptual learning in humans is proposed by allowing comparisons to that of an optimal Bayesian algorithm and a number of suboptimal learning models and provides a flexible framework for future studies to evaluate the optimality of human learning of other visual cues and/or sensory modalities.
Journal ArticleDOI
In vivo positron-emission tomography imaging of progression and transformation in a mouse model of mammary neoplasia.
Craig K. Abbey,Alexander D. Borowsky,Erik T. McGoldrick,Jeffrey P. Gregg,Jeannie E. Maglione,Robert D. Cardiff,Simon R. Cherry +6 more
TL;DR: P positron emission tomography imaging of 2-[18F]-fluoro-deoxy-D-glucose mice is utilized to monitor longitudinal development of mammary intraepithelial neoplasia outgrowths in immunocompetent FVB/NJ mice, promising more effective analysis of tumor progression and reduction of the number of animals needed for statistical power in preclinical therapeutic intervention trials.
Proceedings ArticleDOI
Observer signal-to-noise ratios for the ML-EM algorithm.
TL;DR: In this paper, the authors used an approximate method developed by Barrett, Wilson, and Tsui for finding the ensemble statistics of the Maximum Likelihood-Expectation Maximization algorithm to compute task-dependent figures of merit as a function of stopping point.
Journal ArticleDOI
Comparison of two weighted integration models for the cueing task: linear and likelihood.
TL;DR: The sum of weighted likelihoods model best described the psychophysical results, suggesting that human observers approximate a weighted combination of likelihoods, and not a weighted linear combination.
Proceedings ArticleDOI
Practical issues and methodology in assessment of image quality using model observers
TL;DR: In this paper, the authors review the general methodology and discuss practical issues of using mathematical model observers for task-based assessment of image quality in detection tasks with possible signal and background uncertainty.