39 citations · 50 across the 8 of their papers we have counts for
8 papers
Multimodal and multicontrast image fusion via deep generative models
Giovanna Maria Dimitri, Simeon Spasov, Andrea Duggento +3
Recently, it has become progressively more evident that classic diagnostic labels are unable to reliably describe the complexity and variability of several clinical phenotypes. Thi…
DBGDGM: Dynamic Brain Graph Deep Generative Model
Alexander Campbell, Simeon Spasov, Nicola Toschi +1
Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as fun…
VAESim: A probabilistic approach for self-supervised prototype discovery
Matteo Ferrante, Tommaso Boccato, Simeon Spasov +2
In medicine, curated image datasets often employ discrete labels to describe what is known to be a continuous spectrum of healthy to pathological conditions, such as e.g. the Alzhe…
Contrastive learning for unsupervised medical image clustering and reconstruction
Matteo Ferrante, Tommaso Boccato, Simeon Spasov +2
The lack of large labeled medical imaging datasets, along with significant inter-individual variability compared to clinically established disease classes, poses significant challe…
RicciNets: Curvature-guided Pruning of High-performance Neural Networks Using Ricci Flow
Samuel Glass, Simeon Spasov, Pietro Liò
A novel method to identify salient computational paths within randomly wired neural networks before training is proposed. The computational graph is pruned based on a node mass pro…
GRADE: Graph Dynamic Embedding
Simeon Spasov, Alessandro Di Stefano, Pietro Lio +1
Representation learning of static and more recently dynamically evolving graphs has gained noticeable attention. Existing approaches for modelling graph dynamics focus extensively…