Title: Diffeomorphic Template Transformers. Abstract: In this paper we propose a spatial transformer network where the spatial transformations are limited to the group of diffeomorphisms. Diffeomorphic transformations are a kind of homeomorphism, which by definition preserve topology, a compelling property in certain applications.
We apply this diffemorphic spatial transformer to model the output of a neural network as a topology preserving mapping of a prior shape. By carefully choosing the prior shape we can enforce properties on the output of the network without requiring any changes to the loss function, such as smooth boundaries and a hard constraint on the number of connected components.
The diffeomorphic transformer networks outperform their non-diffeomorphic precursors when applied to learn data invariances in classification tasks. On a breast tissue segmentation task, we show that the approach is robust and flexible enough to deform simple artificial priors, such as Gaussian-shaped prior energies, into high-quality predictive probability densities. In addition to desirable topological properties, the segmentation maps have competitive quantitative fidelity compared to those obtained by direct estimation (i.e. plain U-Net).