Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks

TL;DR

This paper demonstrates that synthetic echocardiography data generated using vision-language diffusion models can improve the performance and efficiency of downstream medical imaging tasks like segmentation and classification.

Contribution

It introduces a novel approach using vision-language diffusion models to generate diverse, realistic synthetic echocardiography data for enhanced medical image analysis.

Findings

Synthetic data improves downstream task accuracy

Enhanced interpretability of models with synthetic data

Faster convergence in training models

Abstract

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}.