Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms Dataset

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).