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1 April 1994 Human and quasi-Bayesian observers of images limited by quantum noise, object-variability, and artifacts
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Abstract
Many investigators have pointed out the need for performance measures that describe how well the images produced by a medical imaging system aid the end user in performing a particular diagnostic task. To this end we have investigated a variety of imaging tasks to determine the applicability of Bayesian and related strategies for predicting human performance. We have compared Bayesian and human classification performance for tasks involving a number of sources of decision-variable spread, including quantum fluctuations contained in the data set, inherent biological variability within each patient class, and deterministic artifacts due to limited data sets.
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Kyle J. Myers, Robert F. Wagner, Kenneth M. Hanson, Harrison H. Barrett, and Jannick P. Rolland "Human and quasi-Bayesian observers of images limited by quantum noise, object-variability, and artifacts", Proc. SPIE 2166, Medical Imaging 1994: Image Perception, (1 April 1994); https://doi.org/10.1117/12.171740
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