  Regularization in convolutional neural networks (CNNs) is usually addressed
with dropout layers. However, dropout is sometimes detrimental in the
convolutional part of a CNN as it simply sets to zero a percentage of pixels in
the feature maps, adding unrepresentative examples during training. Here, we
propose a CNN layer that performs regularization by applying random rotations
of reflections to a small percentage of feature maps after every convolutional
layer. We prove how this concept is beneficial for images with orientational
symmetries, such as in medical images, as it provides a certain degree of
rotational invariance. We tested this method in two datasets, a patch-based set
of histopathology images (PatchCamelyon) to perform classification using a
generic DenseNet, and a set of specular microscopy images of the corneal
endothelium to perform segmentation using a tailored U-net, improving the
performance in both cases.
