A benchmark of six geometric deep learning backends finds transformer-based LaB-GATr most accurate for predicting pressure-derived vFFR fields on patient-specific coronary artery meshes, with pressure drop as the best training target.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Learning Hemodynamic Scalar Fields on Coronary Artery Meshes: A Benchmark of Geometric Deep Learning Models
A benchmark of six geometric deep learning backends finds transformer-based LaB-GATr most accurate for predicting pressure-derived vFFR fields on patient-specific coronary artery meshes, with pressure drop as the best training target.