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Brain infarcts were assessed by a trained image analyst (G.M.P).
Stroke lesions were visually identified, by a trained image analyst, as being hypointense compared to homologous contralateral tissue.
Figure 5 Illustration of training images.
Specifically, we organize the training images into a batch of pairwise samples.
However, training images have never been employed to optimize spatial sampling process.
In this model, neurons learn local features from training images while sub-neurons learn local variations.
Training images can provide multiple-point statistics which describe the statistical relation between multiple spatial locations considered jointly.
The mnist dataset include 60,000 training images and 10,000 test images.
The first 21 images in each class are chosen as training images.
Figure 1 Low-dimensional representations of training images obtained by PCA using the CMU PIE database.
Multiple-point geostatistics can use these process-based models as training images and address the data conditioning problem.
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