  Automatic lesion segmentation in dermoscopy images is an essential step for
computer-aided diagnosis of melanoma. The dermoscopy images exhibits rotational
and reflectional symmetry, however, this geometric property has not been
encoded in the state-of-the-art convolutional neural networks based skin lesion
segmentation methods. In this paper, we present a deeply supervised rotation
equivariant network for skin lesion segmentation by extending the recent group
rotation equivariant network~\cite{cohen2016group}. Specifically, we propose
the G-upsampling and G-projection operations to adapt the rotation equivariant
classification network for our skin lesion segmentation problem. To further
increase the performance, we integrate the deep supervision scheme into our
proposed rotation equivariant segmentation architecture. The whole framework is
equivariant to input transformations, including rotation and reflection, which
improves the network efficiency and thus contributes to the segmentation
performance. We extensively evaluate our method on the ISIC 2017 skin lesion
challenge dataset. The experimental results show that our rotation equivariant
networks consistently excel the regular counterparts with the same model
complexity under different experimental settings. Our best model achieves
77.23\%(JA) on the test dataset, outperforming the state-of-the-art challenging
methods and further demonstrating the effectiveness of our proposed deeply
supervised rotation equivariant segmentation network. Our best model also
outperforms the state-of-the-art challenging methods, which further demonstrate
the effectiveness of our proposed deeply supervised rotation equivariant
segmentation network.
