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).

本仓库为论文《用于合成超声心动图模型训练的高效语义扩散架构》的官方数据集仓库，论文链接：https://www.arxiv.org/abs/2409.19371，配套代码仓库链接：https://github.com/david-stojanovski/echo_from_noise

本次合成数据集依托多种生成架构生成，包括阐明扩散模型（Elucidating Diffusion Model, EDM）、方差爆炸（Variance Exploding, VE）、方差保持（Variance Preserving, VP），以及我们提出的新型模型EDM-L64与EDM-L128。上述模型均采用潜在扩散策略，可大幅降低计算成本。通过集成空间自适应归一化（Spatially Adaptive Normalization, SPADE）模块与基于Γ分布的变分自编码器（Γ-VAE），本数据集生成的图像能够保留深度学习模型训练所需的核心语义特征。

所有预训练分类与分割模型均存放于trained_models文件夹内。所有生成图像均存放于generated_data文件夹中，其中包含CAMUS数据集与原始语义扩散模型（Semantic Diffusion Model, SDM）数据，此外还设有名为easy_inference的文件夹，该文件夹以便捷的目录结构存储全部相关标签图，用于复现本数据集（具体细节可参考对应代码仓库）。