  Whole slide imaging (WSI) refers to the digitization of a tissue specimen
which enables pathologists to explore high-resolution images on a monitor
rather than through a microscope. The formation of tissue folds occur during
tissue processing. Their presence may not only cause out-of-focus digitization
but can also negatively affect the diagnosis in some cases. In this paper, we
have compared five pre-trained convolutional neural networks (CNNs) of
different depths as feature extractors to characterize tissue folds. We have
also explored common classifiers to discriminate folded tissue against the
normal tissue in hematoxylin and eosin (H\&E) stained biopsy samples. In our
experiments, we manually select the folded area in roughly 2.5mm $\times$ 2.5mm
patches at $20$x magnification level as the training data. The ``DenseNet''
with 201 layers alongside an SVM classifier outperformed all other
configurations. Based on the leave-one-out validation strategy, we achieved
$96.3\%$ accuracy, whereas with augmentation the accuracy increased to
$97.2\%$. We have tested the generalization of our method with five unseen WSIs
from the NIH (National Cancer Institute) dataset. The accuracy for patch-wise
detection was $81\%$. One folded patch within an image suffices to flag the
entire specimen for visual inspection.
