May 22, 2023 · In this work, we focus on a limitation of neural network-based atlas building and statistical latent modeling methods, namely that they either are (i) resolution dependent or (ii) …

To highlight the benefit of resolution independence for LDDMM-based data variability modeling, we show that our approach outperforms current neural network–based LDDMM latent code …

Overview of our RDA-INR framework. We combine LDDMM PGA and resolution- independent implicit neural representation (INR) methods used for joint encoding and registration.

May 22, 2023 · Two novel unsupervised diffeomorphic image registration networks are proposed, which use deep Residual Networks (ResNets) as numerical approximations of the underlying …

We introduce a novel model called Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations (RDA-INR) that deals with the aforementioned issues. Figure 1 shows how …

May 22, 2023 · Title: RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations Authors: Sven Dummer , Nicola Strisciuglio , Christoph Brune (Submitted on …

RDA uses two common techniques in sequence: “RDA is a multivariate (meaning multiresponse) multiple linear regression followed by a PCA of the matrix of fitted values” (Borcard et al. 2018, …

In R, a partial RDA is performed in the same way as the RDA we used previously, using rda(). As a demonstration, let’s assess the effect of water chemistry on fish species abundances …

More precisely, our RDA-INR adds resolution-independent implicit neural representations (INRs) to LDDMM PGA and adds Riemannian geometry to deep learning models for shape variability …

Redundancy Analysis (RDA) is a direct extension of multiple regression, as it models the effect of an explanatory matrix X X (n x p) on a response matrix Y Y (n x m). The difference here is that …