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28 March 2013 The equivalence of a human observer and an ideal observer in binary diagnostic tasks
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The Ideal Observer (IO) is “ideal” for given data populations. In the image perception process, as the raw images are degraded by factors such as display and eye optics, there is an equivalent IO (EIO). The EIO uses the statistical information that exits the perception/cognitive degradations as the data. We assume a human observer who received sufficient training, e.g., radiologists, and hypothesize that such a human observer can be modeled as if he is an EIO. To measure the likelihood ratio (LR) distributions of an EIO, we formalize experimental design principles that encourage rationality based on von Neumann and Morgenstern’s (vNM) axioms. We present examples to show that many observer study design refinements, although motivated by empirical principles explicitly, implicitly encourage rationality. Our hypothesis is supported by a recent review paper on ROC curve convexity by Pesce, Metz, and Berbaum. We also provide additional evidence based on a collection of observer studies in medical imaging. EIO theory shows that the “sub-optimal” performance of a human observer can be mathematically formalized in the form of an IO, and measured through rationality encouragement.
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Xin He, Frank Samuelson, Brandon D. Gallas, Berkman Sahiner, and Kyle Myers "The equivalence of a human observer and an ideal observer in binary diagnostic tasks", Proc. SPIE 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment, 86730E (28 March 2013);

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