21 research outputs found

    Projection of images in the SOM GM model for (a) CN and (b) AD example patients from the database.

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    <p>Units indicate the position of ROI centres and value is encoded according to the corresponding colour bar.</p

    Difference image (right column) and ROIs computed by the proposed method (left) for (a) GM and (b) WM, respectively for CN/NOR images.

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    <p>Images from a random training subset from the cross-validation folds have been used. ROIs are coloured according to the colorbar scale due to their relative importance.</p

    Database details.

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    <p>Demographic details of the subjects who participated in this study. Average and standard deviation are given respectively for age, education and MMSE.</p

    Evolution of the patients' diagnosis.

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    <p>Number of subjects whose diagnosis changed to AD during the 6 years after the beginning of the study.</p

    Histograms of the non-parametric test.

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    <p>Histogram of the accuracy rates achieved by using randomly generated neuropsychological scores (1000 repetitions) and the late integration approach. Red lines are the accuracies associated with a -value of . Blue lines are the accuracies for the late integration approach reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088687#pone-0088687-t002" target="_blank">table 2</a>.</p

    Data flow of CAD systems for neurodegenerative disorders.

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    <p>Comparison between the classical approach used in most part of CAD systems for AD and the proposed approach, which consists of taking into account the neuropsychological test data along the brain images. Last three rows show the differences between the ways of integrating in the system the data from the neuroimages and from the neuropsychological tests.</p
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