AIMC Topic: Brain

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Connectional-style-guided contextual representation learning for brain disease diagnosis.

Neural networks : the official journal of the International Neural Network Society
Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI t...

Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning.

Medical image analysis
Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and cli...

Discourse- and lesion-based aphasia quotient estimation using machine learning.

NeuroImage. Clinical
Discourse is a fundamentally important aspect of communication, and discourse production provides a wealth of information about linguistic ability. Aphasia commonly affects, in multiple ways, the ability to produce discourse. Comprehensive aphasia as...

Deep learning denoising reconstruction enables faster T2-weighted FLAIR sequence acquisition with satisfactory image quality.

Journal of medical imaging and radiation oncology
INTRODUCTION: Deep learning reconstruction (DLR) technologies are the latest methods attempting to solve the enduring problem of reducing MRI acquisition times without compromising image quality. The clinical utility of this reconstruction technique ...

Graph Representation Learning for Large-Scale Neuronal Morphological Analysis.

IEEE transactions on neural networks and learning systems
The analysis of neuronal morphological data is essential to investigate the neuronal properties and brain mechanisms. The complex morphologies, absence of annotations, and sheer volume of these data pose significant challenges in neuronal morphologic...

A Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network.

IEEE transactions on neural networks and learning systems
The directed brain functional network construction gives us the new insights into the relationships between brain regions from the causality point of view. The Granger causality analysis is one of the powerful methods to model the directed network. T...

Designing a deep hybridized residual and SE model for MRI image-based brain tumor prediction.

Journal of clinical ultrasound : JCU
Deep learning techniques have become crucial in the detection of brain tumors but classifying numerous images is time-consuming and error-prone, impacting timely diagnosis. This can hinder the effectiveness of these techniques in detecting brain tumo...

A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation.

IEEE transactions on pattern analysis and machine intelligence
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermin...

NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions.

Neuroradiology
PURPOSE: Diffusion magnetic resonance imaging (dMRI) is a widely used non-invasive method for investigating brain anatomical structures. Conventional techniques for estimating fiber orientation distribution (FOD) from dMRI data often neglect voxel-le...