AIMC Topic: Brain
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Deep learning denoising reconstruction enables faster T2-weighted FLAIR sequence acquisition with satisfactory image quality.
Journal of medical imaging and radiation oncology
Apr 5, 2024
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
Apr 4, 2024
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
Apr 4, 2024
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...
Structural or/and functional MRI-based machine learning techniques for attention-deficit/hyperactivity disorder diagnosis: A systematic review and meta-analysis.
Journal of affective disorders
Apr 3, 2024
BACKGROUND: The aim of this study was to investigate the diagnostic value of ML techniques based on sMRI or/and fMRI for ADHD.
Designing a deep hybridized residual and SE model for MRI image-based brain tumor prediction.
Journal of clinical ultrasound : JCU
Apr 3, 2024
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
Apr 3, 2024
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
Apr 2, 2024
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...
BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping.
NeuroImage
Apr 1, 2024
Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms...
Data-driven normative values based on generative manifold learning for quantitative MRI.
Scientific reports
Mar 30, 2024
In medicine, abnormalities in quantitative metrics such as the volume reduction of one brain region of an individual versus a control group are often provided as deviations from so-called normal values. These normative reference values are traditiona...
Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images.
PloS one
Mar 29, 2024
Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Mach...
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