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Found 28 papers, 3 papers with code
Synthesizing study-specific controls using generative models on open access datasets for harmonized multi-study analyses
For one case-control study, we used a generative adversarial model for style-based harmonization to generate site-specific controls.
DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features
Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter.
Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis
The resulting pre-trained models can be adapted to a range of downstream neuroimaging tasks, even when training data for the target task is limited.
Incomplete Multimodal Learning for Complex Brain Disorders Prediction
Especially in studies of brain diseases, research cohorts may include both neuroimaging data and genetic data, but for practical clinical diagnosis, we often need to make disease predictions only based on neuroimages.
A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs
A quality control algorithm is also built-in, trained on the midCC shape features.
A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry
Objective: To build a surface-based model to 1) detect differences between APOE subgroups in patterns of tau deposition and hippocampal atrophy, and 2) use the extracted surface-based features to predict cognitive decline.
Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI
Inspired by the effectiveness of meta-learning for optimizing a model across multiple tasks, here we propose a framework to adapt it to learn across multiple sites.
Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection
In our experiments, two vision transformer architecture variants achieved an AUC of 0. 987 for sex and 0. 892 for AD classification, respectively.
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models
Transfer learning has remarkably improved computer vision.
Curriculum Based Multi-Task Learning for Parkinson's Disease Detection
There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson's disease (PD), a neurodegenerative disease that is difficult to detect in its early stages.
Improved Prediction of Beta-Amyloid and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding
Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics (SPHARM).
Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders.
Secure & Private Federated Neuroimaging
Each site trains the neural network over its private data for some time, then shares the neural network parameters (i. e., weights, gradients) with a Federation Controller, which in turn aggregates the local models, sends the resulting community model back to each site, and the process repeats.
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.
