142 citations · 222 across the 15 of their papers we have counts for
17 papers
Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation
Lucia Innocenti, Michela Antonelli, Francesco Cremonesi +5
Healthcare data is often split into medium/small-sized collections across multiple hospitals and access to it is encumbered by privacy regulations. This brings difficulties to use…
Tackling the dimensions in imaging genetics with CLUB-PLS
Andre Altmann, Ana C Lawry Aguila, Neda Jahanshad +2
A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brai…
Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows
Etrit Haxholli, Marco Lorenzi
While the neural ODE formulation of normalizing flows such as in FFJORD enables us to calculate the determinants of free form Jacobians in O(D) time, the flexibility of the transfo…
Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching
Etrit Haxholli, Marco Lorenzi
In Diffusion Probabilistic Models (DPMs), the task of modeling the score evolution via a single time-dependent neural network necessitates extended training periods and may potenti…
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
Adrià Casamitjana, Marco Lorenzi, Sebastiano Ferraris +6
Joint registration of a stack of 2D histological sections to recover 3D structure (``3D histology reconstruction'') finds application in areas such as atlas building and validation…
Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation
Vien Ngoc Dang, Francesco Galati, Rosa Cortese +7
Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors…
Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets
Jaume Banus, Maxime Sermesant, Oscar Camara +1
The use of mechanistic models in clinical studies is limited by the lack of multi-modal patients data representing different anatomical and physiological processes. For example, ne…
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments
Raphaël Sivera, Hervé Delingette, Marco Lorenzi +2
In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combi…
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data
Clement Abi Nader, Nicholas Ayache, Philippe Robert +1
We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from series of high-dimensional brain images. The model is based on spatio-temp…
Modeling and inference of spatio-temporal protein dynamics across brain networks
Sara Garbarino, Marco Lorenzi
Models of misfolded proteins (MP) aim at discovering the bio-mechanical propagation properties of neurological diseases (ND) by identifying plausible associated dynamical systems.…
DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders
Razvan V. Marinescu, Arman Eshaghi, Marco Lorenzi +5
Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-te…
Disease Knowledge Transfer across Neurodegenerative Diseases
Razvan V. Marinescu, Marco Lorenzi, Stefano B. Blumberg +8
We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal bio…
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data
Santiago Silva, Boris Gutman, Eduardo Romero +3
At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to…
Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes
Clement Abi Nader, Nicholas Ayache, Philippe Robert +1
Alzheimer's disease (AD) is characterized by complex and largely unknown progression dynamics affecting the brain's morphology. Although the disease evolution spans decades, to dat…
Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease
Luigi Antelmi, Nicholas Ayache, Philippe Robert +1
The joint analysis of biomedical data in Alzheimer's Disease (AD) is important for better clinical diagnosis and to understand the relationship between biomarkers. However, jointly…
Constraining the Dynamics of Deep Probabilistic Models
Marco Lorenzi, Maurizio Filippone
We introduce a novel generative formulation of deep probabilistic models implementing "soft" constraints on their function dynamics. In particular, we develop a flexible methodolog…
Disease Progression Modeling and Prediction through Random Effect Gaussian Processes and Time Transformation
Marco Lorenzi, Maurizio Filippone, Daniel C. Alexander +1
The development of statistical approaches for the joint modelling of the temporal changes of imaging, biochemical, and clinical biomarkers is of paramount importance for improving…