AIMC Topic: Brain
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PEARL: Cascaded Self-Supervised Cross-Fusion Learning for Parallel MRI Acceleration.
IEEE journal of biomedical and health informatics
May 6, 2025
Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensive training data. Some self-supervised frameworks, such as deep image prior (DIP), have emerged, elim...
Classification of Neuropsychiatric Disorders via Brain-Region-Selected Graph Convolutional Network.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 5, 2025
For the classification of patients with neuropsychiatric disorders based on rs-fMRI data, this paper proposed a Brain-Region-Selected graph convolutional network (BRS-GCN). In order to effectively identify the most significant biomarkers associated w...
Self-supervised learning for MRI reconstruction through mapping resampled k-space data to resampled k-space data.
Magnetic resonance imaging
May 3, 2025
In recent years, significant advancements have been achieved in applying deep learning (DL) to magnetic resonance imaging (MRI) reconstruction, which traditionally relies on fully sampled data. However, real-world clinical scenarios often demonstrate...
Visualizing functional network connectivity differences using an explainable machine-learning method.
Physiological measurement
May 2, 2025
. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statisti...
Spatiotemporal Implicit Neural Representation for Unsupervised Dynamic MRI Reconstruction.
IEEE transactions on medical imaging
May 2, 2025
Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hi...
How Can Anomalous-Diffusion Neural Networks Under Connectomics Generate Optimized Spatiotemporal Dynamics.
IEEE transactions on neural networks and learning systems
May 2, 2025
Spatiotemporal dynamics in the brain have been recognized as strongly related to the formation of perceived and cognitive diseases, such as delusions and hallucinations in Alzheimer's disease. However, two practical considerations are rarely mentione...
Modality-Aware Discriminative Fusion Network for Integrated Analysis of Brain Imaging Genomics.
IEEE transactions on neural networks and learning systems
May 2, 2025
Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD), characterized by subtle clinical symptoms that pose challenges for accurate diagnosis. The quest for the identification of MCI individuals has highlighted the impo...
SAGN: Sparse Adaptive Gated Graph Neural Network With Graph Regularization for Identifying Dual-View Brain Networks.
IEEE transactions on neural networks and learning systems
May 2, 2025
Due to the absence of a gold standard for threshold selection, brain networks constructed with inappropriate thresholds risk topological degradation or contain noise connections. Therefore, graph neural networks (GNNs) exhibit weak robustness and ove...
Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification.
IEEE transactions on neural networks and learning systems
May 2, 2025
The brain signal classification is the basis for the implementation of brain-computer interfaces (BCIs). However, most existing brain signal classification methods are based on signal processing technology, which require a significant amount of manua...
Artifact estimation network for MR images: effectiveness of batch normalization and dropout layers.
BMC medical imaging
May 1, 2025
BACKGROUND: Magnetic resonance imaging (MRI) is an essential tool for medical diagnosis. However, artifacts may degrade images obtained through MRI, especially owing to patient movement. Existing methods that mitigate the artifact problem are subject...
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