Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms Dataset
Contributors
- 1. King's College London
- 2. Ultromics
Description
This is the official data repository for the paper: "Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms", available at: https://www.arxiv.org/abs/2409.19371. The corresponding code is available at: https://github.com/david-stojanovski/echo_from_noise
The synthetic data is produced using a variety of generative architectures, including the Elucidating Diffusion Model (EDM), Variance Exploding (VE), Variance Preserving (VP), and our novel models, EDM-L64 and EDM-L128, which employ latent diffusion strategies to significantly reduce computational cost. By incorporating spatially adaptive normalization (SPADE) blocks and Γ-distribution-based Variational Autoencoders (Γ-VAE), these datasets ensure that the generated images preserve the essential semantic features required for training deep learning models.
All pretrained classification and segmentation models can be found within the trained_models file.
All generated images can be found within the generated_data file. Included is the CAMUS and original Semantic Diffusion Model (SDM) data, as well as a folder labelled easy_inference designed to contain all relevant labelmaps in a convenient folder for generating replicas of the dataset (detailed at codebase).
Files
generated_data.zip
Files
(20.9 GB)
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md5:151c09785e5e29c92bb9f4c21fd0cd93
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md5:c8b1a1db4d70b8718cc54e968c3905be
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Additional details
Related works
- Continues
- 10.1007/978-3-031-44521-7_4 (DOI)
Dates
- Created
-
2024-09
Software
- Repository URL
- https://github.com/david-stojanovski/EDMLX
- Programming language
- Python