Search Results for author:
Found 16 papers, 2 papers with code
Secure Federated Learning for Neuroimaging
Specifically, we investigate training neural models to classify Alzheimer's disease, and estimate Brain Age, from magnetic resonance imaging datasets distributed across multiple sites, including heterogeneous environments where sites have different amounts of data, statistical distributions, and computational capabilities.
Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy
We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption
Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location.
Membership Inference Attacks on Deep Regression Models for Neuroimaging
In particular, we show that it is possible to infer if a sample was used to train the model given only access to the model prediction (black-box) or access to the model itself (white-box) and some leaked samples from the training data distribution.
Improved Brain Age Estimation with Slice-based Set Networks
Deep Learning for neuroimaging data is a promising but challenging direction.
3D Grid-Attention Networks for Interpretable Age and Alzheimer's Disease Prediction from Structural MRI
Building on a 3D convolutional neural network, we added two attention modules at different layers of abstraction, so that features learned are spatially related to the global features for the task.
Overview of Scanner Invariant Representations
Pooled imaging data from multiple sources is subject to bias from each source.
Scanner Invariant Representations for Diffusion MRI Harmonization
Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation.
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.
Measures of Tractography Convergence
In the present work, we use information theory to understand the empirical convergence rate of tractography, a widely-used approach to reconstruct anatomical fiber pathways in the living brain.
Simultaneous Matrix Diagonalization for Structural Brain Networks Classification
This paper considers the problem of brain disease classification based on connectome data.
Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model
Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature.
Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression
Then we select the relevant group features by performing the group Lasso feature selection process in a sequence of parameters.
A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex
One of the primary objectives of human brain mapping is the division of the cortical surface into functionally distinct regions, i. e. parcellation.
An Empirical Study of Continuous Connectivity Degree Sequence Equivalents
In the present work we demonstrate the use of a parcellation free connectivity model based on Poisson point processes.
Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions
To the best of our knowledge, this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD.
