11 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.LG2025★ 11 cited
Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights
Cosmin-Andrei Hatfaludi, Alex Serban
Federated learning has the potential to unlock siloed data and distributed resources by enabling collaborative model training without sharing private data. As more complex foundati…
cs.CV2025
ConceptVAE: Self-Supervised Fine-Grained Concept Disentanglement from 2D Echocardiographies
Costin F. Ciusdel, Alex Serban, Tiziano Passerini
While traditional self-supervised learning methods improve performance and robustness across various medical tasks, they rely on single-vector embeddings that may not capture fine-…
cs.CV2024
Generative 3D Cardiac Shape Modelling for In-Silico Trials
Andrei Gasparovici, Alex Serban
We propose a deep learning method to model and generate synthetic aortic shapes based on representing shapes as the zero-level set of a neural signed distance field, conditioned by…