We lifted 8 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| sukjulian/coronary-mesh-convolution | canonical | 6 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| norm | Ran | sukjulian/coronary-mesh-convolution/diffusion_net/geometry.py code served (permissive licence) · get_code("6261ff5e682d8867") |
| norm2 | Ran | sukjulian/coronary-mesh-convolution/diffusion_net/geometry.py code served (permissive licence) · get_code("169c2afe3d0207b9") |
| normalize | Ran | sukjulian/coronary-mesh-convolution/diffusion_net/geometry.py code served (permissive licence) · get_code("7246dcec4dc84360") |
| parallel_to_serial | Ran | sukjulian/coronary-mesh-convolution/utils/model_tools.py code served (permissive licence) · get_code("42635d2a53e53dfa") |
| random_rotate_points | Ran | sukjulian/coronary-mesh-convolution/diffusion_net/utils.py code served (permissive licence) · get_code("92d9f3f36c3257f5") |
| toNP | Ran | sukjulian/coronary-mesh-convolution/diffusion_net/utils.py code served (permissive licence) · get_code("4de0071f24c6454b") |
| label_smoothing_log_loss | Not yet run | sukjulian/coronary-mesh-convolution/diffusion_net/utils.py code served (permissive licence) · get_code("3249bbcbe19754c6") |
| load | Not yet run | sukjulian/coronary-mesh-convolution/utils/model_tools.py code served (permissive licence) · get_code("dcb5649d1f399453") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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. Machine-learning methods that estimate blood flow in individual patients could accelerate or replace CFD simulation to overcome these limitations. In this work, we consider the estimation of vector-valued quantities on the wall of three-dimensional geometric artery models. We employ group equivariant graph convolution in an end-to-end SE(3)-equivariant neural network that operates directly on triangular surface meshes and makes efficient use of training data. We run experiments on a large dataset of synthetic coronary arteries and find that our method estimates directional wall shear stress (WSS) with an approximation error of 7.6% and normalised mean absolute error (NMAE) of 0.4% while up to two orders of magnitude faster than CFD. Furthermore, we show that our method is powerful enough to accurately predict transient, vector-valued WSS over the cardiac cycle while conditioned on a range of different inflow boundary conditions. These results demonstrate the potential of our proposed method as a plugin replacement for CFD in the personalised prediction of hemodynamic vector and scalar fields.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2212.05023")
get_code_for_paper("2212.05023")
have("2212.05023")
Connect an agent — have() is free.