DIFF$$\cdot $$3: A Latent Diffusion Model for the Generation of Synthetic 3D Echocardiographic Images and Corresponding Labels

Author:

Ferdian Edward,Zhao Debbie,Talou Gonzalo D. Maso,Quill Gina M.,Legget Malcolm E.,Doughty Robert N.,Nash Martyn P.,Young Alistair A.

Publisher

Springer Nature Switzerland

Reference29 articles.

1. Akrout, M., et al.: Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images (2023). arXiv:2301.04802

2. Alessandrini, M., et al.: A pipeline for the generation of realistic 3D synthetic echocardiographic sequences: methodology and open-access database. IEEE Trans. Medical Imaging 34(7), 1436–1451 (2015). https://doi.org/10.1109/TMI.2015.2396632

3. Alzubaidi, L.,et al.: MedNet: pre-trained convolutional neural network model for the medical imaging tasks. CoRR abs/2110.0 (2021) arXiv:2110.06512

4. Chambon, P., Bluethgen, C., Langlotz, C.P., Chaudhari, A.: Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains (10 2022). arxiv:2210.04133

5. DuMont Schütte, A., et al.: Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation. NPJ Digital Medicine 4(1), 141 (2021). https://doi.org/10.1038/s41746-021-00507-3

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