https://github.com/cardiacvision/diffusion

Diffusion generative modeling of excitable media

https://github.com/cardiacvision/diffusion

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Repository

Diffusion generative modeling of excitable media

Basic Info
  • Host: GitHub
  • Owner: cardiacvision
  • Language: Python
  • Default Branch: main
  • Size: 1.03 MB
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  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
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Created over 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme

README.md

# Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models [![https://arxiv.org/abs/2312.14830](http://img.shields.io/badge/paper-arxiv.2312.14830-B31B1B.svg)](https://arxiv.org/abs/2312.14830)

Description

This repository contains all the code files used to generate the results from the paper. Each folder corresponds to the code for different portions of the paper, and has a corresponding requirements.txt file. Each folder also has an associated README.md file containing installation information and information on how to run the models.

| Tasks | Base Folder | Command to Run Training | |--------|----------------------------|------------------------------------------------------------| | Task 1 | palette-diffusion/ | python run.py -c config/conditional.json -p train | | Task 2 | point-voxel-diffusion/ | python train_generation.py | | Task 3 | palette-diffusion/ | python run.py -c config/next_timestep.json -p train | | Task 4 | palette-diffusion/ | python run.py -c config/spiral_3d.json -p train | | Task 5 | palette-diffusion/ | python run.py -c config/inpainting_2d_time.json -p train | | Task 6 | unconditional-diffusion/ | bash script.sh |

Citation

@article{baranwal2023dreaming, title={Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models}, author={Tanish Baranwal and Jan Lebert and Jan Christoph}, year={2023}, eprint={2312.14830}, archivePrefix={arXiv}, primaryClass={physics.med-ph} }

Acknowledgements

We are benefiting a lot from the following projects: - Janspiry/Palette-Image-to-Image-Diffusion-Models - alexzhou907/PVD - huggingface/diffusers

Owner

  • Name: Cardiac Vision Laboratory
  • Login: cardiacvision
  • Kind: organization
  • Location: United States of America

Cardiovascular Research Institute, University of California, San Francisco

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Dependencies

palette-diffusion/MR_perceptual/Dockerfile docker
  • nvidia/cuda 9.0-base-ubuntu16.04 build
grad-cam/requirements.txt pypi
  • grad-cam ==1.5.0
  • matplotlib *
  • numpy *
  • scikit-image *
  • torch ==2.0.1
  • torchvision ==0.15.2a0
  • tqdm *
palette-diffusion/MR_perceptual/requirements.txt pypi
  • IPython *
  • jupyter *
  • matplotlib >=1.5.1
  • numpy >=1.14.3
  • opencv-python >=2.4.11
  • pytorch_msssim *
  • scikit-image *
  • scikit-image >=0.13.0
  • scipy >=1.0.1
  • torch >=0.4.0
  • torchvision >=0.2.1
  • tqdm >=4.28.1
palette-diffusion/MR_perceptual/setup.py pypi
  • torch >=0.4.0
palette-diffusion/requirements.txt pypi
  • clean-fid *
  • numpy *
  • opencv-python *
  • pandas *
  • scipy *
  • tensorboardX >=1.14
  • torch >=1.6
  • torchvision *
  • tqdm *
point-voxel-generation/metrics/ChamferDistancePytorch/chamfer2D/setup.py pypi
point-voxel-generation/metrics/ChamferDistancePytorch/chamfer3D/setup.py pypi
point-voxel-generation/metrics/ChamferDistancePytorch/chamfer5D/setup.py pypi
point-voxel-generation/metrics/PyTorchEMD/setup.py pypi
point-voxel-generation/requirements.txt pypi
  • Pillow ==6.2.1
  • Shapely ==1.7.0
  • conda *
  • cudatoolkit ==10.1
  • descartes ==1.1.0
  • fire ==0.3.1
  • jupyter ==1.0.0
  • kaolin ==0.1.0
  • lutorpy =1.3.7
  • matplotlib *
  • numba =0.51.2
  • open3d *
  • opencv_python ==4.3.0
  • pip *
  • pycuda =2019.1.2
  • python ==3.6
  • pytorch3d ==0.2.5
  • torch ==1.4.0
  • torch-cluster ==1.5.4
  • torch-scatter ==2.0.4
  • torch-sparse ==0.6.1
  • torch-spline-conv ==1.2.0
  • torch_geometric ==1.6.0
  • torchvision ==0.5.0
  • xmltodict =0.12.0
point-voxel-generation/sphviz/pyproject.toml pypi
point-voxel-generation/sphviz/setup.py pypi
  • PyQt5 *
  • cmocean *
  • ffmpeg-python *
  • imageio-ffmpeg *
  • matplotlib *
  • more-itertools *
  • numpy *
  • pyacvd *
  • pygem *
  • pymeshfix *
  • pyvistaqt *
  • scipy *
  • sphtools *
  • tqdm *
point-voxel-generation/sphviz/sphtools.egg-info/requires.txt pypi
  • PyQt5 *
  • cmocean *
  • ffmpeg-python *
  • imageio-ffmpeg *
  • matplotlib *
  • more-itertools *
  • numpy *
  • pymeshfix *
  • pyvistaqt *
  • scipy *
  • tqdm *
unconditional-diffusion/requirements.txt pypi
  • Pillow *
  • accelerate >=0.16.0
  • datasets *
  • diffusers ==0.27.2
  • matplotlib *
  • numpy *
  • pytorch-fid ==0.3.0
  • torch ==2.3.0
  • torchvision *
  • torchvision ==0.18.0
  • tqdm *
unet-models/requirements.txt pypi
  • matplotlib *
  • numpy *
  • scikit-image *
  • tensorflow ==2.8.0
  • tqdm *