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Found 26 papers, 14 papers with code
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications
Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security.
Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation
To the best of our knowledge, this is the first work in which CBM, such as label fusion techniques, are used to solve a problem of collaborative learning.
Tackling the dimensions in imaging genetics with CLUB-PLS
The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes.
On Tail Decay Rate Estimation of Loss Function Distributions
The study of loss function distributions is critical to characterize a model's behaviour on a given machine learning problem.
Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows
Furthermore, we derive the Jacobian determinant of the general augmented form by generalizing the chain rule in the continuous sense into the Cable Rule, which expresses the forward sensitivity of ODEs with respect to their initial conditions.
Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching
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 potentially impede modeling flexibility and capacity.
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security.
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models
In addition, thanks to the variational nature of Fed-MIWAE, our method is designed to perform multiple imputation, allowing for the quantification of the imputation uncertainty in the federated scenario.
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
Machine Unlearning (MU) is an increasingly important topic in machine learning safety, aiming at removing the contribution of a given data point from a training procedure.
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates
We show that our general framework applies to existing optimization schemes including centralized learning, FedAvg, asynchronous FedAvg, and FedBuff.
Privacy Preserving Image Registration
Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame.
Ranked #1 on
Cubic splines Image Registration
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Affine Image Registration
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A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations
Our method is expressed through a hierarchical Bayesian latent variable model, where client-specific parameters are assumed to be realization from a global distribution at the master level, which is in turn estimated to account for data bias and variability across clients.
A General Theory for Client Sampling in Federated Learning
In this work, we provide a general theoretical framework to quantify the impact of a client sampling scheme and of the clients heterogeneity on the federated optimization.
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
This work addresses the problem of optimizing communications between server and clients in federated learning (FL).
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images.
Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation
First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image.
Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets
To tackle this problem we introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models, with application to brain studies with incomplete heart data.
Free-rider Attacks on Model Aggregation in Federated Learning
Free-rider attacks against federated learning consist in dissimulating participation to the federated learning process with the goal of obtaining the final aggregated model without actually contributing with any data.
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments
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.
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data
We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from series of high-dimensional brain images.
Modeling and inference of spatio-temporal protein dynamics across brain networks
Models of misfolded proteins (MP) aim at discovering the bio-mechanical propagation properties of neurological diseases (ND) by identifying plausible associated dynamical systems.
Disease Knowledge Transfer across Neurodegenerative Diseases
DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases.
DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders
Here we present DIVE: Data-driven Inference of Vertexwise Evolution.
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data
At this moment, databanks worldwide contain brain images of previously unimaginable numbers.
Constraining the Dynamics of Deep Probabilistic Models
We introduce a novel generative formulation of deep probabilistic models implementing "soft" constraints on their function dynamics.
