Vision-Language Synthetic Data Enhances Echocardiography Downstream
  Tasks
Pooria Ashrafian Milad Yazdani Moein Heidari Dena Shahriari Ilker Hacihaliloglu
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{https://github.com/Pooria90/DiffEcho}{GitHub}.