Search Results for author:
Found 7 papers, 5 papers with code
LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshes
We propose LaB-GATr, a transfomer neural network with geometric tokenisation that can effectively learn with large-scale (bio-)medical surface and volume meshes through sequence compression and interpolation.
SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks
Hence, SIRE can be trained with arbitrarily oriented vessels with varying radii to generalise to vessels with a wide range of calibres and tortuosity.
Generative modeling of living cells with SO(3)-equivariant implicit neural representations
Finally, we show how microscopy images of living cells that correspond to our generated cell shapes can be synthesized using an image-to-image model.
SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets
We demonstrate how to construct an SE(3)-equivariant GNN that is independent of the spatial orientation of the input mesh and show how this reduces the necessary amount of training data compared to a baseline neural network.
Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall
Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice.
Implicit Neural Representations for Generative Modeling of Living Cell Shapes
Deep generative models for cell shape synthesis require a light-weight and flexible representation of the cell shape.
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
In this work, we propose to instead use mesh convolutional neural networks that directly operate on the same finite-element surface mesh as used in CFD.
