On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

Trend · papers per month

count % of corpus

9 results for “voxelwise”

Learning an MR acquisition-invariant representation using Siamese neural networkscs.CV

MRAI-NET learns MRI scanner-independent features for better tissue segmentation.

problem Invariance of MRI voxelwise classifiers across different scanners.

method Siamese neural network to extract acquisition-invariant feature vectors.

result MRAI-NET outperforms traditional classifiers in small sample settings.

MRI Siamese networks invariance tissue segmentation neural networks

2018-10-17abs ↗pdf ↗

Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modelingstat.ML

Tool models nonlinear brain changes across Alzheimer's spectrum.

problem Limited GLM methods for nonlinear neuroimaging analysis.

method Voxelwise nonlinear regression for brain atrophy modeling.

result Distinct nonlinear brain atrophy patterns in Alzheimer's disease.

neuroimaging voxelwise nonlinear regression Alzheimer's disease brain atrophy

2016-12-02abs ↗pdf ↗

Flexible Amortized Variational Inference in qBOLD MRIeess.IV

This work improves the accuracy of OEF and DBV maps from qBOLD MRI data.

problem Noisy and inaccurate inference of OEF and DBV from qBOLD MRI data.

method Flexible amortized variational inference using a scaled multivariate logit-Normal distribution.

result Smooth and physiologically plausible OEF and DBV maps inferred with fewer training data.

qBOLD MRI Oxygen Extraction Fraction Deoxygenated Blood Volume Variational Inference Logit-Normal Distribution

2022-03-11abs ↗pdf ↗

A critical reappraisal of predicting suicidal ideation using fMRIq-bio.NC

Study finds fMRI can predict suicidal ideation, but analysis questions results.

problem Predicting suicidal ideation using fMRI data.

method Naive Bayes classifier trained on fMRI responses to words related to mortality.

result Classification accuracy of 91% for predicting suicidal ideation, but analysis calls into question the accuracy of the findings.

suicide prediction fMRI machine learning overfitting neuroimaging

2021-03-10abs ↗pdf ↗

Surrogate Modelling of Proton Dose with Monte Carlo Dropout Uncertainty Quantificationstat.ML

Develops a neural surrogate for proton dose calculation using Monte Carlo dropout uncertainty.

problem Computational demand in proton therapy workflows requiring repeated evaluations.

method Integrates Monte Carlo dropout into a neural network surrogate for fast, differentiable dose predictions and uncertainty quantification.

result Shows significant speedups over MC while retaining uncertainty information.

proton therapy surrogate modeling Monte Carlo dropout uncertainty quantification neural networks

2025-09-16abs ↗pdf ↗

MR Acquisition-Invariant Representation Learningcs.CV

A neural network learns MRI scan-invariant features for brain tissue classification.

problem Lack of generalization in voxelwise classification methods due to scanner differences.

method Siamese neural network (MRAI-net) to learn acquisition-invariant representations.

result Linear classifier outperforms CNNs on limited training data for tissue classification.

MRI Neural Networks Representation Learning Tissue Classification Generalization

2017-09-22abs ↗pdf ↗

MRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption of Independent Pixelseess.IV

New MRI method maps tissue parameters more accurately by ignoring voxel independence.

problem Voxel independence assumption limits model fitting reliability and repeatability.

method Self-supervised deep variational approach with Gaussian mixture prior.

result Our method outperforms current techniques in dMRI simulations and real data.

MRI parameter mapping Gaussian mixture VAE quantitative MRI diffusion MRI

2024-11-16abs ↗pdf ↗

Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE)stat.ML

PS-VAE extracts multi-parameter MRI biomarkers with uncertainty quantification.

problem Uncertainty in inverse problems limits clinical acceptance of quantitative MRI methods.

method Physics-Structured Variational Autoencoder (PS-VAE) integrating physics simulator and self-supervised learning.

result PS-VAE provides full covariance of inter-parameter correlations and accelerates multi-parametric MRI quantification.

MRI Quantitative Imaging Uncertainty Quantification Variational Autoencoder Physics-Structured

2026-02-03abs ↗pdf ↗

DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorderscs.CV

DIVE models brain disease progression with high spatial resolution.

problem Reconstruct long-term brain pathology from short-term data.

method Clusters vertex-wise biomarker measurements, estimates average trajectories, and identifies disease-specific patterns.

result Reveals distinct patterns of pathology in different diseases and biomarker types.

neurodegenerative disorders brain pathology disease progression image-based modeling cortical surface

2019-01-11abs ↗pdf ↗