Search Results for author:
Found 4 papers, 3 papers with code
Neural Operator Learning for Ultrasound Tomography Inversion
Neural operator learning as a means of mapping between complex function spaces has garnered significant attention in the field of computational science and engineering (CS&E).
Modeling the Shape of the Brain Connectome via Deep Neural Networks
The goal of diffusion-weighted magnetic resonance imaging (DWI) is to infer the structural connectivity of an individual subject's brain in vivo.
Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics
In order to enable population-level statistical analysis of the structural connectome, we propose representing a connectome as a Riemannian metric, which is a point on an infinite-dimensional manifold.
Structural Connectome Atlas Construction in the Space of Riemannian Metrics
The structural connectome is often represented by fiber bundles generated from various types of tractography.
