Hybrid Diffusion Model for Breast Ultrasound Image Augmentation

1. 1Combine text-to-image generation with image-to-image refinement to create a hybrid diffusion model
2. 2Fine-tune the model using low-rank adaptation (LoRA) and textual inversion (TI) for improved performance
3. 3Apply the hybrid diffusion model to breast ultrasound image datasets to generate augmented images with improved visual fidelity
4. 4Evaluate the quality and diversity of the generated images using metrics such as PSNR and SSIM

AI engineers and researchers working on medical imaging projects can benefit from this approach to improve the quality and diversity of breast ultrasound image datasets, which can lead to better model performance and more accurate diagnoses

💡 Combining text-to-image generation with image-to-image refinement can improve the quality and diversity of breast ultrasound image datasets

💡 Hybrid diffusion model for breast ultrasound image augmentation improves visual fidelity and preserves ultrasound texture!