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Found 12 papers, 7 papers with code
Multivariate Wasserstein Functional Connectivity for Autism Screening
Most approaches to the estimation of brain functional connectivity from the functional magnetic resonance imaging (fMRI) data rely on computing some measure of statistical dependence, or more generally, a distance between univariate representative time series of regions of interest (ROIs) consisting of multiple voxels.
Artificial Text Detection via Examining the Topology of Attention Maps
The impressive capabilities of recent generative models to create texts that are challenging to distinguish from the human-written ones can be misused for generating fake news, product reviews, and even abusive content.
Convolutional neural networks for automatic detection of Focal Cortical Dysplasia
Focal cortical dysplasia (FCD) is one of the most common epileptogenic lesions associated with cortical development malformations.
Fader Networks for domain adaptation on fMRI: ABIDE-II study
ABIDE is the largest open-source autism spectrum disorder database with both fMRI data and full phenotype description.
Domain Shift in Computer Vision models for MRI data analysis: An Overview
Machine learning and computer vision methods are showing good performance in medical imagery analysis.
Interpretation of 3D CNNs for Brain MRI Data Classification
Deep learning shows high potential for many medical image analysis tasks.
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem
Segmentation of tumors in brain MRI images is a challenging task, where most recent methods demand large volumes of data with pixel-level annotations, which are generally costly to obtain.
3D Deformable Convolutions for MRI classification
Deep learning convolutional neural networks have proved to be a powerful tool for MRI analysis.
Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry
We consider a problem of diagnostic pattern recognition/classification from neuroimaging data.
fMRI: preprocessing, classification and pattern recognition
As machine learning continues to gain momentum in the neuroscience community, we witness the emergence of novel applications such as diagnostics, characterization, and treatment outcome prediction for psychiatric and neurological disorders, for instance, epilepsy and depression.
Machine Learning in Appearance-based Robot Self-localization
The latter allows solving the robot localization problem as the Kalman filtering problem.
Conformal k-NN Anomaly Detector for Univariate Data Streams
Anomalies in time-series data give essential and often actionable information in many applications.
