Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models
Journal:
arXiv
Published Date:
Jan 25, 2025
Abstract
Ultrasound imaging is widely used in medical diagnosis, especially for fetal
health assessment. However, the availability of high-quality annotated
ultrasound images is limited, which restricts the training of machine learning
models. In this paper, we investigate the use of diffusion models to generate
synthetic ultrasound images to improve the performance on fetal plane
classification. We train different classifiers first on synthetic images and
then fine-tune them with real images. Extensive experimental results
demonstrate that incorporating generated images into training pipelines leads
to better classification accuracy than training with real images alone. The
findings suggest that generating synthetic data using diffusion models can be a
valuable tool in overcoming the challenges of data scarcity in ultrasound
medical imaging.
