39 citations · 63 across the 13 of their papers we have counts for
13 papers
Brain Captioning: Decoding human brain activity into images and text
Matteo Ferrante, Furkan Ozcelik, Tommaso Boccato +2
Every day, the human brain processes an immense volume of visual information, relying on intricate neural mechanisms to perceive and interpret these stimuli. Recent breakthroughs i…
Beyond Multilayer Perceptrons: Investigating Complex Topologies in Neural Networks
Tommaso Boccato, Matteo Ferrante, Andrea Duggento +1
In this study, we explore the impact of network topology on the approximation capabilities of artificial neural networks (ANNs), with a particular focus on complex topologies. We p…
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…
Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli
Matteo Ferrante, Tommaso Boccato, Nicola Toschi
Brain decoding is a field of computational neuroscience that uses measurable brain activity to infer mental states or internal representations of perceptual inputs. Therefore, we p…
BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks
Matteo Ferrante, Tommaso Boccato, Nicola Toschi
The willingness to trust predictions formulated by automatic algorithms is key in a vast number of domains. However, a vast number of deep architectures are only able to formulate…
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…
Physically constrained neural networks to solve the inverse problem for neuron models
Matteo Ferrante, Andera Duggento, Nicola Toschi
Systems biology and systems neurophysiology in particular have recently emerged as powerful tools for a number of key applications in the biomedical sciences. Nevertheless, such mo…
DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning
Alexander Campbell, Antonio Giuliano Zippo, Luca Passamonti +2
Graph neural networks (GNNs) have demonstrated success in learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data. However, existing…
4Ward: a Relayering Strategy for Efficient Training of Arbitrarily Complex Directed Acyclic Graphs
Tommaso Boccato, Matteo Ferrante, Andrea Duggento +1
Thanks to their ease of implementation, multilayer perceptrons (MLPs) have become ubiquitous in deep learning applications. The graph underlying an MLP is indeed multipartite, i.e.…
An intertwined neural network model for EEG classification in brain-computer interfaces
Andrea Duggento, Mario De Lorenzo, Stefano Bargione +4
The brain computer interface (BCI) is a nonstimulatory direct and occasionally bidirectional communication link between the brain and a computer or an external device. Classically,…
Towards a predictive spatio-temporal representation of brain data
Tiago Azevedo, Luca Passamonti, Pietro Liò +1
The characterisation of the brain as a "connectome", in which the connections are represented by correlational values across timeseries and as summary measures derived from graph t…