54 research outputs found
Summary of results.
<p>Shown are out-of-bootstrap accuracy, sensitivity, specificity, and AUC (mean and 95% CI [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197992#pone.0197992.ref059" target="_blank">59</a>]), along with .632+ error [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197992#pone.0197992.ref058" target="_blank">58</a>] based on 100 bootstrapped trials using the best performing feature-based classifiers (all with RF feature selection) as well as those of the scalar metrics from univariate logistic regression.</p
Comparisons of ROCs based on the testing dataset for the total of 7 classifiers.
<p>Comparisons of ROCs based on the testing dataset for the total of 7 classifiers.</p
Illustration of the training and validation error functions and the corresponding validation accuracy.
<p>(Top): Error functions from three deep learning training trials; (Bottom): the corresponding validation accuracy (based on the 10% training dataset used for validation internally), vs. training epochs for three randomly generated trials. Maximum validation accuracies based on validation datasets were achieved using an early-stopping criterion after 2000 epochs.</p
Probability maps for WM voxels selected.
<p>(a and b): using the F-score or (c and d) RF-based approach based on 58 independent feature selections. In each trial, the two approaches selected 4% and 1%, respectively, of the WM voxels as features. To improve visualization, only voxels with a probability greater than 50% (i.e., selected by at least 29 times) are shown. For the RF-based approach, SLF-R and EC-L were two dominant regions often selected for classification. See Matlab figure (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197992#pone.0197992.s001" target="_blank">S1 Fig</a>) in the <b>supplementary</b> for interactive visualization.</p
Summary of the dimensions of the weights and offset parameters, along with the normalization functions used to define the deep learning network.
<p>See <b>Appendix</b> for details regarding the normalization functions.</p
Cumulative WM fiber strains on representative orthogonal planes for a pair of striking (non-injury) and struck (concussed) athletes.
<p>Cumulative WM fiber strains on representative orthogonal planes for a pair of striking (non-injury) and struck (concussed) athletes.</p
Performance summary of the best performing feature-based classifiers (all with RF feature selection) as well as of the four scalar metrics from univariate logistic regression.
<p>Accuracy, sensitivity, specificity and AUC were reported based on the 58 separate injury predictions in the leave-one-out cross-validation framework. The average AUC measures (and 95% CI) for the training datasets were also reported.</p
The Worcester Head Injury Model (WHIM).
<p>Shown are the head exterior (a) and intracranial components (b), along with peak fiber strain-encoded rendering of the segmented WM outer surface (c). The <i>x</i>-, <i>y</i>-, and <i>z</i>-axes of the model coordinate system correspond to the posterior–anterior, right–left, and inferior–superior direction, respectively. The strain image volume, which was used to generate the rendering within the co-registered head model for illustrative purposes, directly served as input signals for deep learning network training and concussion classification (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197992#pone.0197992.g002" target="_blank">Fig 2</a>).</p
Summary of results.
<p>Shown are leave-one-out cross-validation accuracy, sensitivity, and specificity based on the testing dataset for the three feature-based machine learning classifiers. No feature selection was conducted and WM voxels of the entire brain were used for classification. For RF, the 95% confidence intervals (CI) were also reported based on the 100 random trials.</p
KM and Cox information in testing set for all the top multidimensional biomarkers.
<p>KM and Cox information in testing set for all the top multidimensional biomarkers.</p
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