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Proceedings Paper

Higher-order tasks: Human vs. machine performance
Author(s): R. F. Wagner; K. J. Myers; D. G. Brown; M. J. Tapiovaara; A. E. Burgess
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Paper Abstract

The linear prewhitening matched filter (PWMF) is the optimal decision function for discriminating exactly-specified signals in additive Gaussian noise. When the signals are less well specified the optimal decision function contains higher than linear terms in the data. Several examples of detection and discrimination tasks are presented for which only the linear term is required and other examples for which higher order terms are necessary. Even in the nonlinear case decision functions can often be approximated by linear operations on the data followed by logical operations. This combination of linear weights plus logic operations is typical of neural network models, which are thought to be elementary models of human processing mechanisms. A number of experiments suggest that there is no decrease in performance of humans for some complex tasks that ideally require such nonlinear operations. However, there are other such tasks where human performance is degraded and this appears to be due more to the complexity of the task and the nature of the correlations in the image than the order of the task. This suggests applications in diagnostic imaging where it might be advantageous for a machine viewer to substitute or work in conjunction with the human observer.

Paper Details

Date Published: 1 May 1989
PDF: 13 pages
Proc. SPIE 1090, Medical Imaging III: Image Formation, (1 May 1989); doi: 10.1117/12.953203
Show Author Affiliations
R. F. Wagner, Center for Devices & Radiological Health, FDA (United States)
K. J. Myers, Center for Devices & Radiological Health, FDA (United States)
D. G. Brown, Center for Devices & Radiological Health, FDA (United States)
M. J. Tapiovaara, Finnish Centre for Radiation and Nuclear Safety (STUK) (Finland)
A. E. Burgess, University of British Columbia (Canada)

Published in SPIE Proceedings Vol. 1090:
Medical Imaging III: Image Formation
Samuel J. Dwyer; R. Gilbert Jost; Roger H. Schneider, Editor(s)

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