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Article overview
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A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval | | Yilan Zhang
; Fengying Xie
; Xuedong Song
; Hangning Zhou
; Yiguang Yang
; Haopeng Zhang
; Jie Liu
; | | Date: |
1 Aug 2022 | | 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 main direction, indicating
that there are a large number of skin lesion target rotations in the datasets.
However, CNNs lack anti-rotation ability, which is bound to affect the feature
extraction ability of CNNs. We propose a rotation meanout (RM) network to
extract rotation invariance features from dermoscopy images. In RM, each set of
rotated feature maps corresponds to a set of weight-sharing convolution outputs
and they are fused using meanout operation to obtain the final feature maps.
Through theoretical derivation, the proposed RM network is rotation-equivariant
and can extract rotation-invariant features when being 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 proposed 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. | | Source: | arXiv, 2208.00627 | | Services: | Forum | Review | PDF | Favorites | |
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