Multimodal medical image fusion plays an important role in clinical applications. However, multimodal medical image fusion methods ignore the feature dependence among modals, and the feature fusion ability with different granularity is not strong. A ...
Advanced 3D imaging techniques and image segmentation and classification methods can transform biomedical research by offering insights into the human brain cytoarchitecture under pathological conditions. We propose a comprehensive pipeline for 3D im...
Spiking Neural Networks (SNNs), designed to more accurately model the brain's neurobiological processes, have been proposed as energy-efficient alternatives to conventional Artificial Neural Networks (ANNs), which typically incur high computational a...
Autism is a genetically and clinically very heterogeneous group of disorders. Gene co-expression network analysis can help unravel its complex genetic architecture through the identification of communities of genes that are dysregulated. Using a publ...
The abnormal growth of cells inside or near the brain is called a brain tumor. Brain tumors can be benign (non-cancerous) or malignant (cancerous). Both these types can exert pressure on the surrounding brain tissue, increasing intracranial pressure....
Early identification of altered brain networks in neonates at risk for neurodevelopmental impairments is critical for timely intervention and improving outcomes. This study explores the potential of graph neural networks (GNNs) applied to structural ...
OBJECTIVE: Freezing of gait (FOG) is a common gait disorder in the advanced stages of Parkinson's disease (PD), closely associated with impaired balance and executive function. This study aimed to investigate specific changes in brain glucose metabol...
Automated brain tumor detection represents a fundamental challenge in contemporary medical imaging, demanding both precision and computational feasibility for practical implementation. This research introduces a novel Vision Transformer (ViT) framewo...
While previous studies have reported functional abnormalities in the prefrontal-limbic-subcortical circuit, the treatment effects on this activity remain unclear. This longitudinal study aimed to investigate spontaneous brain activity in bipolar diso...
Functional magnetic resonance imaging (fMRI) is crucial for identifying neurological disorder biomarkers, but current deep learning methods face some limitations. Template-dependent methods reliant on fixed brain atlases lack inter-subject specificit...
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