DiFUSAL: Diffusion-Based Fetal Ultrasound Synthesis with Active Learning
Arjemandi, Maryam Hassan, Salma Wang, Hu Valappil, Saudabi Yaqub, Mohammad
Arjemandi, Maryam
Hassan, Salma
Wang, Hu
Valappil, Saudabi
Yaqub, Mohammad
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
The availability of high-quality fetal ultrasound data is vastly scarce, constrained by privacy concerns and the rarity of publicly accessible datasets. In addition, ultrasound is noisy, operator-dependent, and sensitive to patients’ variability. These limitations hinder the development of robust diagnostic models in maternal-fetal healthcare. We introduce DiFUSAL, a generative framework that integrates diffusion models with self-guided active learning for the automated synthesis of fetal ultrasound images. DiFUSAL estimates key biometric features such as gestational age and uses them to generate structured clinical-report-style prompts. These prompts are used to condition the diffusion model, enabling targeted and realistic image generation aligned with real-world diagnostic scenarios. This approach removes the need for human feedback or manual fine-tuning, making the process scalable and adaptable. Our method achieves an average LPIPS of 0.3660 ± 0.0442, outperforming standard baselines in perceptual similarity. We further demonstrate the utility of the generated data in two downstream tasks: fetal plane classification and gestational age prediction. DiFUSAL enhances model generalization and performance across both tasks. By generating clinically meaningful synthetic data, DiFUSAL contributes to automated interpretation, ultrasound training, point-of-care support, and foundation model development. Code and data will be made available at GitHub.
Citation
M. Arjemandi, S. Hassan, H. Wang, S. Valappil, and M. Yaqub, “DiFUSAL: Diffusion-Based Fetal Ultrasound Synthesis with Active Learning,” pp. 130–139, 2026, doi: 10.1007/978-3-032-06329-8_13
Source
Simplifying Medical Ultrasound
Conference
6th International Workshop on Simplifying Medical Ultrasound, ASMUS 2025
Keywords
Fetal Ultrasound, Synthetic Data, Stable Diffusion, Active Learning, Medical Imaging, Data Augmentation
Subjects
Source
6th International Workshop on Simplifying Medical Ultrasound, ASMUS 2025
Publisher
Springer Nature