activity
20242026
most citedSynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions

2 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Auditing Patient Privacy in Medical Generative Models: Scalable Memorization Detection with DeepSSIM++

Antonio Scardace, Francesco Guarnera, Sebastiano Battiato +1

While deep generative models offer new opportunities for medical image synthesis and data sharing, their ability to memorize and reproduce training samples raises serious concerns…

cs.CV2025

Temporally-Aware Diffusion Model for Brain Progression Modelling with Bidirectional Temporal Regularisation

Mattia Litrico, Francesco Guarnera, Mario Valerio Giuffrida +2

Generating realistic MRIs to accurately predict future changes in the structure of brain is an invaluable tool for clinicians in assessing clinical outcomes and analysing the disea…

cs.CV2025

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…

cs.CV2025

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…

eess.IV2024★ 2 cited

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…

eess.IV2024

TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI

Mattia Litrico, Francesco Guarnera, Valerio Giuffirda +2

Generating realistic images to accurately predict changes in the structure of brain MRI is a crucial tool for clinicians. Such applications help assess patients' outcomes and analy…