Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks
This work addresses data scarcity in medical imaging, specifically for echocardiography analysis, by providing a method to generate synthetic data, though it appears incremental as it builds on existing diffusion and vision-language models.
The paper tackles the challenge of limited high-quality data for deep learning in medical ultrasound by using vision-language models to generate synthetic echocardiography images, showing that this synthetic data enhances accuracy and interpretability in downstream tasks like segmentation and classification with improved metrics and faster convergence.
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}.