  Developing robust deep learning models for fetal ultrasound image analysis
requires comprehensive, high-quality datasets to effectively learn informative
data representations within the domain. However, the scarcity of labelled
ultrasound images poses substantial challenges, especially in low-resource
settings. To tackle this challenge, we leverage synthetic data to enhance the
generalizability of deep learning models. This study proposes a diffusion-based
method, Fetal Ultrasound LoRA (FU-LoRA), which involves fine-tuning latent
diffusion models using the LoRA technique to generate synthetic fetal
ultrasound images. These synthetic images are integrated into a hybrid dataset
that combines real-world and synthetic images to improve the performance of
zero-shot classifiers in low-resource settings. Our experimental results on
fetal ultrasound images from African cohorts demonstrate that FU-LoRA
outperforms the baseline method by a 13.73% increase in zero-shot
classification accuracy. Furthermore, FU-LoRA achieves the highest accuracy of
82.40%, the highest F-score of 86.54%, and the highest AUC of 89.78%. It
demonstrates that the FU-LoRA method is effective in the zero-shot
classification of fetal ultrasound images in low-resource settings. Our code
and data are publicly accessible on https://github.com/13204942/FU-LoRA.
