23 citations · 27 across the 9 of their papers we have counts for
11 papers
Reinforcing the Weakest Links: Modernizing SIENA with Targeted Deep Learning Integration
Riccardo Raciti, Lemuel Puglisi, Francesco Guarnera +2
Percentage Brain Volume Change (PBVC) derived from Magnetic Resonance Imaging (MRI) is a widely used biomarker of brain atrophy, with SIENA among the most established methods for i…
GenTract: Generative Global Tractography
Alec Sargood, Lemuel Puglisi, Elinor Thompson +2
Tractography is the process of inferring the trajectories of white-matter pathways in the brain from diffusion magnetic resonance imaging (dMRI). Local tractography methods, which…
A Novel Metric for Detecting Memorization in Generative Models for Brain MRI Synthesis
Antonio Scardace, Lemuel Puglisi, Francesco Guarnera +2
Deep generative models have emerged as a transformative tool in medical imaging, offering substantial potential for synthetic data generation. However, recent empirical studies hig…
CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis
Alec Sargood, Lemuel Puglisi, James H. Cole +3
Synthesizing amyloid PET scans from the more widely available and accessible structural MRI modality offers a promising, cost-effective approach for large-scale Alzheimer's Disease…
Benchmarking GANs, Diffusion Models, and Flow Matching for T1w-to-T2w MRI Translation
Andrea Moschetto, Lemuel Puglisi, Alec Sargood +4
Magnetic Resonance Imaging (MRI) enables the acquisition of multiple image contrasts, such as T1-weighted (T1w) and T2-weighted (T2w) scans, each offering distinct diagnostic insig…
Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion
Lemuel Puglisi, Daniel C. Alexander, Daniele Ravì
The growing availability of longitudinal Magnetic Resonance Imaging (MRI) datasets has facilitated Artificial Intelligence (AI)-driven modeling of disease progression, making it po…
Industrial Machines Health Prognosis using a Transformer-based Framework
David J Poland, Lemuel Puglisi, Daniele Ravi
This article introduces Transformer Quantile Regression Neural Networks (TQRNNs), a novel data-driven solution for real-time machine failure prediction in manufacturing contexts. O…
SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions
Lemuel Puglisi, Alessia Rondinella, Linda De Meo +3
Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age D…
Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge
Lemuel Puglisi, Daniel C. Alexander, Daniele Ravì
In this work, we introduce Brain Latent Progression (BrLP), a novel spatiotemporal disease progression model based on latent diffusion. BrLP is designed to predict the evolution of…
DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification
Lemuel Puglisi, Frederik Barkhof, Daniel C. Alexander +3
Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within the…
An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training
Daniele Ravi, Frederik Barkhof, Daniel C. Alexander +3
Large medical imaging data sets are becoming increasingly available, but ensuring sample quality without significant artefacts is challenging. Existing methods for identifying impe…