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The Effective Set-Size model has been used to describe uncertainty in various signal detection experiments. The model regards images as if they were an effective number (M*) of searchable locations, where the observer treats each location as a location-known-exactly detection task with signals having average detectability d'. The model assumes a rational observer behaves as if he searches an effective number of independent locations and follows signal detection theory at each location. Thus the location-known-exactly detectability (d') and the effective number of independent locations M* fully characterize search performance. In this model the image rating in a single-response task is assumed to be the maximum response that the observer would assign to these many locations. The model has been used by a number of other researchers, and is well corroborated. We examine this model as a way of differentiating imaging tasks that radiologists perform. Tasks involving more searching or location uncertainty may have higher estimated M* values. In this work we applied the Effective Set-Size model to a number of medical imaging data sets. The data sets include radiologists reading screening and diagnostic mammography with and without computer-aided diagnosis (CAD), and breast tomosynthesis. We developed an algorithm to fit the model parameters using two-sample maximum-likelihood ordinal regression, similar to the classic bi-normal model. The resulting model ROC curves are rational and fit the observed data well. We find that the distributions of M* and d' differ significantly among these data sets, and differ between pairs of imaging systems within studies. For example, on average tomosynthesis increased readers’ d' values, while CAD reduced the M* parameters. We demonstrate that the model parameters M* and d' are correlated. We conclude that the Effective Set-Size model may be a useful way of differentiating location uncertainty from the diagnostic uncertainty in medical imaging tasks.
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