Sept. 16, 2022, 1:15 a.m. | Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F da Costa, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardo

cs.CV updates on arXiv.org arxiv.org

Deep neural networks have brought remarkable breakthroughs in medical image
analysis. However, due to their data-hungry nature, the modest dataset sizes in
medical imaging projects might be hindering their full potential. Generating
synthetic data provides a promising alternative, allowing to complement
training datasets and conducting medical image research at a larger scale.
Diffusion models recently have caught the attention of the computer vision
community by producing photorealistic synthetic images. In this study, we
explore using Latent Diffusion Models to generate …

arxiv brain brain imaging diffusion diffusion models imaging

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne