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.
@article{arxiv.2603.26834,
title = {Hybrid Diffusion Model for Breast Ultrasound Image Augmentation},
author = {Farhan Fuad Abir and Sanjeda Sara Jennifer and Niloofar Yousefi and Laura J. Brattain},
journal= {arXiv preprint arXiv:2603.26834},
year = {2026}
}
Comments
Accepted at IEEE International Symposium on Biomedical Imaging (ISBI) 2026