AIMC Topic: Brain
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Learnable Brain Connectivity Structures for Identifying Neurological Disorders.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 28, 2024
Brain networks/graphs have been widely recognized as powerful and efficient tools for identifying neurological disorders. In recent years, various graph neural network models have been developed to automatically extract features from brain networks. ...
A 3D Convolutional Neural Network Based on Non-enhanced Brain CT to Identify Patients with Brain Metastases.
Journal of imaging informatics in medicine
Aug 26, 2024
Dedicated brain imaging for cancer patients is seldom recommended in the absence of symptoms. There is increasing availability of non-enhanced CT (NE-CT) of the brain, mainly owing to a wider utilization of Positron Emission Tomography-CT (PET-CT) in...
MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.
IEEE transactions on cybernetics
Aug 26, 2024
Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. De...
Metabolic signatures derived from whole-brain MR-spectroscopy identify early tumor progression in high-grade gliomas using machine learning.
Journal of neuro-oncology
Aug 24, 2024
PURPOSE: Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resona...
Deep learning-based segmentation in MRI-(immuno)histological examination of myelin and axonal damage in normal-appearing white matter and white matter hyperintensities.
Brain pathology (Zurich, Switzerland)
Aug 23, 2024
The major vascular cause of dementia is cerebral small vessel disease (SVD). Its diagnosis relies on imaging hallmarks, such as white matter hyperintensities (WMH). WMH present a heterogenous pathology, including myelin and axonal loss. Yet, these mi...
RobMedNAS: searching robust neural network architectures for medical image synthesis.
Biomedical physics & engineering express
Aug 23, 2024
Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis o...
Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.
IEEE journal of translational engineering in health and medicine
Aug 23, 2024
The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the eff...
On the application of hybrid deep 3D convolutional neural network algorithms for predicting the micromechanics of brain white matter.
Computer methods and programs in biomedicine
Aug 22, 2024
BACKGROUND: Material characterization of brain white matter (BWM) is difficult due to the anisotropy inherent to the three-dimensional microstructure and the various interactions between heterogeneous brain-tissue (axon, myelin, and glia). Developing...
Separating group- and individual-level brain signatures in the newborn functional connectome: A deep learning approach.
NeuroImage
Aug 22, 2024
Recent studies indicate that differences in cognition among individuals may be partially attributed to unique brain wiring patterns. While functional connectivity (FC)-based fingerprinting has demonstrated high accuracy in identifying adults, early s...
XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG.
NeuroImage
Aug 22, 2024
Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging...
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