The upper plot shows the overall accuracy as a function of iteration and epoch number. The blue line plot (no markers) shows training dataset accuracy while the black line plot (dashed with circle markers) shows validation accuracy. The lower plot shows the loss function (red with no markers: training loss; black with circle markers: validation loss. (TIF)</p
The heatmaps show the accuracy of logistic regression models trained on one quantization gray-level ...
<p><b>(A)</b> Receiver operating characteristic (ROC) curve. The solid black line indicates the medi...
(A) Visualization of the entire classification training process. After ground truth data were select...
(a) Model without image enhancement and transfer learning; (b) Model without image enhancement, but ...
Accuracy means ± standard error (n = 50) is displayed for training with SNV (left), SV (middle), and...
<p>If the number of colors used is 4, this means only the 4 most frequent colors from 7 extracted co...
<p>Accuracy on the training and validation sets as a function of the number of steps of training. Tr...
<p>Plot shows the pairwise differences in performance among classifiers. The horizontal scale shows ...
Taking 75% voxels as training set, and the remaining 25% as validation set. After 20000 iterations, ...
<p>We set a maximum of ten epochs since a prolongation to more training epochs yields no gain in per...
We show here the validation loss (normalized from 0 to 1) as a function of epoch number throughout C...
To evaluate the performance of classifiers that were trained on a wide range of quantizations, 100 l...
<p>(A) Classification accuracy when the number of channels was reduced. The bold black, red, and blu...
<p>(Top): Error functions from three deep learning training trials; (Bottom): the corresponding vali...
<p>(dot, average accuracy statistics of the 50 random samples; error bar, one standard deviation awa...
The heatmaps show the accuracy of logistic regression models trained on one quantization gray-level ...
<p><b>(A)</b> Receiver operating characteristic (ROC) curve. The solid black line indicates the medi...
(A) Visualization of the entire classification training process. After ground truth data were select...
(a) Model without image enhancement and transfer learning; (b) Model without image enhancement, but ...
Accuracy means ± standard error (n = 50) is displayed for training with SNV (left), SV (middle), and...
<p>If the number of colors used is 4, this means only the 4 most frequent colors from 7 extracted co...
<p>Accuracy on the training and validation sets as a function of the number of steps of training. Tr...
<p>Plot shows the pairwise differences in performance among classifiers. The horizontal scale shows ...
Taking 75% voxels as training set, and the remaining 25% as validation set. After 20000 iterations, ...
<p>We set a maximum of ten epochs since a prolongation to more training epochs yields no gain in per...
We show here the validation loss (normalized from 0 to 1) as a function of epoch number throughout C...
To evaluate the performance of classifiers that were trained on a wide range of quantizations, 100 l...
<p>(A) Classification accuracy when the number of channels was reduced. The bold black, red, and blu...
<p>(Top): Error functions from three deep learning training trials; (Bottom): the corresponding vali...
<p>(dot, average accuracy statistics of the 50 random samples; error bar, one standard deviation awa...
The heatmaps show the accuracy of logistic regression models trained on one quantization gray-level ...
<p><b>(A)</b> Receiver operating characteristic (ROC) curve. The solid black line indicates the medi...
(A) Visualization of the entire classification training process. After ground truth data were select...