Hybrid Diffusion Model for Breast Ultrasound Image Augmentation

• URL: http://arxiv.org/abs/2603.26834v1
• Date: Fri, 27 Mar 2026 05:29:41 GMT
• Title: Hybrid Diffusion Model for Breast Ultrasound Image Augmentation
• Abstract summary: We propose a hybrid diffusion-based augmentation framework to overcome the challenge of ultrasound data augmentation in datasets.<n>Our approach improves visual fidelity and preserves ultrasound texture by combining text-to-image generation with image-to-image refinement.<n>Our method generated realistic, class-consistent images on an open-source Kaggle breast ultrasound image dataset.
• Score: 2.087144931454977
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a hybrid diffusion-based augmentation framework to overcome the critical challenge of ultrasound data augmentation in breast ultrasound (BUS) datasets. Unlike conventional diffusion-based augmentations, our approach improves visual fidelity and preserves ultrasound texture by combining text-to-image generation with image-to-image (img2img) refinement, as well as fine-tuning with low-rank adaptation (LoRA) and textual inversion (TI). Our method generated realistic, class-consistent images on an open-source Kaggle breast ultrasound image dataset (BUSI). Compared to the Stable Diffusion v1.5 baseline, incorporating TI and img2img refinement reduced the Frechet Inception Distance (FID) from 45.97 to 33.29, demonstrating a substantial gain in fidelity while maintaining comparable downstream classification performance. Overall, the proposed framework effectively mitigates the low-fidelity limitations of synthetic ultrasound images and enhances the quality of augmentation for robust diagnostic modeling.

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