A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval

TL;DR

This paper introduces a rotation meanout (RM) network that enhances CNNs with rotation invariance for dermoscopy image classification and retrieval, improving robustness and accuracy without increasing model complexity.

Contribution

The paper proposes a novel rotation meanout (RM) operation that makes CNN features rotation-invariant, applicable to any CNN architecture without adding parameters.

Findings

Outperforms existing anti-rotation methods in dermoscopy tasks.

Achieves significant improvements in classification accuracy.

Enhances retrieval performance with rotation-invariant features.

Abstract

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.