AIMC Topic: Brain
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A Plug-In Graph Neural Network to Boost Temporal Sensitivity in fMRI Analysis.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Learning-based methods offer performance leaps over traditional methods in classification analysis of high-dimensional functional MRI (fMRI) data. In this domain, deep-learning models that analyze functional connectivity (FC) features among brain reg...
H-Net: Heterogeneous Neural Network for Multi-Classification of Neuropsychiatric Disorders.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Clinical studies have proved that both structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are implicitly associated with neuropsychiatric disorders (NDs), and integrating multi-modal to the binary classifica...
HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehen...
Brain tumor detection and segmentation using deep learning.
Magma (New York, N.Y.)
Sep 4, 2024
OBJECTIVES: Brain tumor detection, classification and segmentation are challenging due to the heterogeneous nature of brain tumors. Different deep learning-based algorithms are available for object detection; however, the performance of detection alg...
The application value of Rs-fMRI-based machine learning models for differentiating mild cognitive impairment from Alzheimer's disease: a systematic review and meta-analysis.
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Sep 3, 2024
BACKGROUND: Various machine learning (ML) models based on resting-state functional MRI (Rs-fMRI) have been developed to facilitate differential diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the diagnostic accurac...
Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks.
Journal of neuroscience methods
Sep 3, 2024
BACKGROUND: Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distribut...
Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning.
PLoS computational biology
Sep 3, 2024
Understanding the mechanism by which the brain achieves relatively consistent information processing contrary to its inherent inconsistency in activity is one of the major challenges in neuroscience. Recently, it has been reported that the consistenc...
Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging.
IEEE transactions on medical imaging
Sep 3, 2024
Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recentl...
FPL+: Filtered Pseudo Label-Based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation.
IEEE transactions on medical imaging
Sep 3, 2024
Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised Domain Adaptation (UDA) is appealing where only unlabeled images are ne...
Brain Emotion Perception Inspired EEG Emotion Recognition With Deep Reinforcement Learning.
IEEE transactions on neural networks and learning systems
Sep 3, 2024
Inspired by the well-known Papez circuit theory and neuroscience knowledge of reinforcement learning, a double dueling deep Q network (DQN) is built incorporating the electroencephalogram (EEG) signals of the frontal lobe as prior information, which ...
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