Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks

arXiv:2403.19880v1 Announce Type: cross
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, …

arxiv cs.cv data eess.iv language synthetic synthetic data tasks type vision