bibtext: @InProceedings{ Dom_Diffusion_MICCAI2024,
                   author = { Domínguez, Marina and Velikova, Yordanka and Navab, Nassir and Azampour, Mohammad Farid },
                   title = { { Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image Generation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
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.
title: Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image Generation
authors: Domínguez, Marina Velikova, Yordanka Navab, Nassir Azampour, Mohammad Farid