Diffusion as Sound Propagation: Physics-Inspired Model for Ultrasound Image Generation

Marina Domínguez, Yordanka Velikova, Nassir Navab, Mohammad Farid Azampour

Abstract: Deep learning (DL) methods typically require large datasets to effectively learn data distributions. However, in the medical field, data is often limited in quantity, and acquiring labeled data can be costly. To mitigate this data scarcity, data augmentation techniques are commonly employed. Among these techniques, generative models play a pivotal role in expanding datasets. However, when it comes to ultrasound (US) imaging, the authenticity of generated data often diminishes due to the oversight of ultrasound physics.

External IDs:dblp:conf/miccai/DominguezVNA24