AIMultimediaLab

Check out our latest results in generating medical images via a latent-to-image approach in conditional . Apart from the high quality of the results, i.e., doctors cannot differentiate the generated vs real images, our approach ensures -preserving capabilities, i.e., synthetic medical images tend to contain traces of the original real training images, which may lead to revealing the identity of the source – see A.-G. Andrei, M.G. Constantin, M. Graziani, H. Müller, B. Ionescu, “Privacy Preserving Histopathological Image Augmentation with Conditional Generative Adversarial Networks”, Pattern Recognition Letters, Impact Factor 3.9, 2025 https://lnkd.in/dzurYV69 Universitatea POLITEHNICA din București HES-SO Valais-Wallis.

our special issue on Realistic Data: , , organized with ACM Transactions on Multimedia Computing, Communications and Applications, guest editors Bogdan Ionescu, Ioannis Patras, Henning Müller, Alberto Del Bimbo, is now online https://lnkd.in/dDtWR9Es – it is a timely collection of contributions targeting the various facets and modalities, e.g., signals, images, volumes, audio, etc., of the current trends in generating synthetic data, i.e., data which looks as real as it gets but it is computer generated. We received a record number of 40 submissions out of which 15 were finally accepted for publication. Enjoy your reading! Universitatea POLITEHNICA din București, Queen Mary University of London, HES-SO, Università degli Studi di Firenze.

… and it started, the 1st on for https://www.aimultimedialab.ro/1st-workshop-on-artificial-intelligence-for-multimedia/ -- the yearly workshop of the AI Multimedia Lab https://www.aimultimedialab.ro/. The workshop is dedicated to the PhD students of our lab who are presenting in front of the other colleagues, with the objective of sharing ideas and results as well as brainstorming. Wonderful people doing science! Campus Research Institute Universitatea POLITEHNICA din București