  The computer-aided diagnosis (CAD) system can provide a reference basis for
the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs)
can not only extract visual elements such as colors and shapes but also
semantic features. As such they have made great improvements in many tasks of
dermoscopy images. The imaging of dermoscopy has no principal orientation,
indicating that there are a large number of skin lesion rotations in the
datasets. However, CNNs lack rotation invariance, which is bound to affect the
robustness of CNNs against rotations. To tackle this issue, we propose a
rotation meanout (RM) network to extract rotation-invariant features from
dermoscopy images. In RM, each set of rotated feature maps corresponds to a set
of outputs of the weight-sharing convolutions and they are fused using meanout
strategy to obtain the final feature maps. Through theoretical derivation, the
proposed RM network is rotation-equivariant and can extract rotation-invariant
features when followed by the global average pooling (GAP) operation. The
extracted rotation-invariant features can better represent the original data in
classification and retrieval tasks for dermoscopy images. The RM is a general
operation, which does not change the network structure or increase any
parameter, and can be flexibly embedded in any part of CNNs. Extensive
experiments are conducted on a dermoscopy image dataset. The results show our
method outperforms other anti-rotation methods and achieves great improvements
in dermoscopy image classification and retrieval tasks, indicating the
potential of rotation invariance in the field of dermoscopy images.
