High-quality, large-scale data is essential for robust deep learning models
in medical applications, particularly ultrasound image analysis. Diffusion
models facilitate high-fidelity medical image generation, reducing the costs
associated with acquiring and annotating new images. This paper utilizes recent
vision-language models to produce diverse and realistic synthetic
echocardiography image data, preserving key features of the original images
guided by textual and semantic label maps. Specifically, we investigate three
potential avenues: unconditional generation, generation guided by text, and a
hybrid approach incorporating both textual and semantic supervision. We show
that the rich contextual information present in the synthesized data
potentially enhances the accuracy and interpretability of downstream tasks,
such as echocardiography segmentation and classification with improved metrics
and faster convergence. Our implementation with checkpoints, prompts, and the
created synthetic dataset will be publicly available at
\href{this https URL}{GitHub}.

利用最近的视觉 - 语言模型，通过文本和语义标签图引导，产生多样和逼真的合成心脏超声图像数据，以保留原始图像的关键特征。研究了无条件生成、文本引导生成和混合方法，并展示出合成数据中存在的丰富上下文信息可能提高下游任务的准确性和可解释性，例如改善指标和更快收敛的心脏超声图像分割和分类。实现部分含有检查点、提示和创建的合成数据集可公开获取。