Search Results for author: Paul M. Thompson

Found 16 papers, 2 papers with code

Secure Federated Learning for Neuroimaging

no code implementations11 May 2022 Dimitris Stripelis, Umang Gupta, Hamza Saleem, Nikhil Dhinagar, Tanmay Ghai, Rafael Sanchez, Chrysovalantis Anastasiou, Armaghan Asghar, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

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.

Federated Learning

Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption

no code implementations7 Aug 2021 Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

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.

Federated Learning

Membership Inference Attacks on Deep Regression Models for Neuroimaging

no code implementations6 May 2021 Umang Gupta, Dimitris Stripelis, Pradeep K. Lam, Paul M. Thompson, José Luis Ambite, Greg Ver Steeg

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.

Federated Learning

3D Grid-Attention Networks for Interpretable Age and Alzheimer's Disease Prediction from Structural MRI

no code implementations18 Nov 2020 Pradeep Lam, Alyssa H. Zhu, Iyad Ba Gari, Neda Jahanshad, Paul M. Thompson

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.

Disease Prediction

Scanner Invariant Representations for Diffusion MRI Harmonization

no code implementations10 Apr 2019 Daniel Moyer, Greg Ver Steeg, Chantal M. W. Tax, Paul M. Thompson

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.

Fairness Image Reconstruction

Measures of Tractography Convergence

no code implementations12 Jun 2018 Daniel Moyer, Paul M. Thompson, Greg Ver Steeg

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.

A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex

1 code implementation2 Mar 2017 Daniel Moyer, Boris A. Gutman, Neda Jahanshad, Paul M. Thompson

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

no code implementations18 Nov 2016 Daniel Moyer, Boris A. Gutman, Joshua Faskowitz, Neda Jahanshad, Paul M. Thompson

In the present work we demonstrate the use of a parcellation free connectivity model based on Poisson point processes.

Point Processes

Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions

no code implementations19 Aug 2016 Qingyang Li, Tao Yang, Liang Zhan, Derrek Paul Hibar, Neda Jahanshad, Yalin Wang, Jieping Ye, Paul M. Thompson, Jie Wang

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.

Model Selection

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