Abstract: We propose a semantic segmentation model for histopathology that exploits rotation and reflection symmetries inherent in histopathology images. We demonstrate significant performance gains due to increased weight sharing, as well as improvements in predictive stability. The group-equivariant CNN framework is extended for segmentation by introducing a new (G -> Z2)-convolution that transforms feature maps on a group to planar feature maps. In addition, equivariant transposed convolution is formulated for up-sampling in an encoder-decoder network. We further show the importance of exploiting more symmetries by varying the size of the group.
Keywords: semantic segmentation, digital pathology, G-CNN
Author Affiliation: University of Amsterdam, Koninklijke Philips, Qualcomm, CIFAR