AIMC Topic: Brain
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A flexible 2.5D medical image segmentation approach with in-slice and cross-slice attention.
Computers in biology and medicine
Sep 23, 2024
Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high computational efficiency. However, segmenting 2.5D images, characterized...
Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS.
Nature computational science
Sep 23, 2024
Reproducible definition and identification of cell types is essential to enable investigations into their biological function and to understand their relevance in the context of development, disease and evolution. Current approaches model variability...
Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging.
NeuroImage
Sep 22, 2024
Diffusion magnetic resonance imaging (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure reconstruction techniques require a large number of diffusion gradients, which is not clinically feas...
Differential plasma cytokine variation following X-ray or proton brain irradiation using machine-learning approaches.
Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Sep 21, 2024
PURPOSE: X-ray and proton irradiation have been reported to induce distinct modifications in cytokine expression in vitro and in vivo, suggesting a dissimilar inflammatory response between X-rays and protons. We aimed to investigate the differences i...
Fourier Convolution Block with global receptive field for MRI reconstruction.
Medical image analysis
Sep 20, 2024
Reconstructing images from under-sampled Magnetic Resonance Imaging (MRI) signals significantly reduces scan time and improves clinical practice. However, Convolutional Neural Network (CNN)-based methods, while demonstrating great performance in MRI ...
Artificial intelligence-based analysis of behavior and brain images in cocaine-self-administered marmosets.
Journal of neuroscience methods
Sep 19, 2024
BACKGROUND: The sophisticated behavioral and cognitive repertoires of non-human primates (NHPs) make them suitable subjects for studies involving cocaine self-administration (SA) schedules. However, ethical considerations, adherence to the 3Rs princi...
Nanorobot-Based Direct Implantation of Flexible Neural Electrode for BCI.
IEEE transactions on bio-medical engineering
Sep 19, 2024
Brain-Computer Interface (BCI) has gained remarkable prominence in biomedical community. While BCI holds vast potential across diverse domains, the implantation of neural electrodes poses multifaceted challenges to fully explore the power of BCI. Con...
An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network.
IEEE transactions on bio-medical engineering
Sep 19, 2024
OBJECTIVE: Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a universally effective model for everyone is impractical due to substantial interindividual variability in...
Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.
Psychiatry and clinical neurosciences
Sep 18, 2024
BACKGROUND: Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in br...
PD-ARnet: a deep learning approach for Parkinson's disease diagnosis from resting-state fMRI.
Journal of neural engineering
Sep 18, 2024
. The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD.This ...
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