Jiale Cheng, Xin Zhang, Fenqiang Zhao, Zhengwang Wu, Ya Wang, Ying Huang, Weili Lin, Li Wang, Gang Li
Brain cortical surfaces, which have an intrinsic spherical topology, are typically represented by triangular meshes and mapped onto a spherical manifold in neuroimaging analysis. Inspired by the strong capability of feature learning in Convolutional Neural Networks (CNNs), spherical CNNs have been developed accordingly and achieved many successes in cortical surface analysis. Motivated by the recent success of the transformer, in this paper, for the first of time, we extend the transformer into the spherical space and propose the spherical transformer, which can better learn contextual and structural features than spherical CNNs...
March 2022: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro