39 citations · 42 across the 7 of their papers we have counts for
7 papers
LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics
Edoardo Coppola, Stefano Fiorini, Pietro Liò +2
Point-of-care cardiac devices such as smartwatches and handheld ECG recorders typically capture 1--2 leads, yet existing ECG foundation models are architecturally constrained to fi…
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…
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…
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution
James King, Ramon Viñas Torné, Alexander Campbell +1
There have been several successful deep learning models that perform audio super-resolution. Many of these approaches involve using preprocessed feature extraction which requires a…
High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm
Lorena Qendro, Alexander Campbell, Pietro Liò +1
Electroencephalography (EEG) is crucial for the monitoring and diagnosis of brain disorders. However, EEG signals suffer from perturbations caused by non-cerebral artifacts limitin…
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…