Generative Model Based Medical Data Augmentation for Chronic Venous Insufficiency

In this study, we propose a medical ultrasound image data augmentation method based on a fine-tuned Stable Diffusion model. Due to limited access to medical data, especially for conditions like Chronic Venous Insufficiency (CVI), data scarcity remains a critical challenge for developing deep learning-based diagnostic systems. To address this, we finetuned a pretrained Stable Diffusion model using a small number of labeled patient and non-patient ultrasound images. Two class-specific text prompts were used to condition the model for generating anatomically distinct images. The results show that the model can generate qualitatively realistic ultrasound images, especially for patient cases, where some diagnostic features such as artery-vein pairs were partially reproduced. However, the model struggled to consistently replicate key features, particularly in the non-patient class with limited training samples. This limitation highlights the need for future work to incorporate explicit diagnostic annotations and structural constraints to improve the clinical applicability of generated data. Our approach demonstrates the potential of generative models in medical image synthesis.