  As a training and analysis strategy for convolutional neural networks (CNNs),
we slice images into tiled segments and use, for training and prediction,
segments that both satisfy a criterion of information diversity and contain
sufficient content to support classification. In particular, we utilize image
entropy as the diversity criterion. This ensures that each tile carries as much
information diversity as the original image, and for many applications serves
as an indicator of usefulness in classification. To make predictions, a
probability aggregation framework is applied to probabilities assigned by the
CNN to the input image tiles. This technique facilitates the use of large,
high-resolution images that would be impractical to analyze unmodified;
provides data augmentation for training, which is particularly valuable when
image availability is limited; and the ensemble nature of the input for
prediction enhances its accuracy.
