AIMC Topic: Brain
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RS-Net: An end-to-end deep learning framework for rodent skull stripping in multi-center brain MRI.
NeuroImage
Aug 8, 2024
Skull stripping is a crucial preprocessing step in magnetic resonance imaging (MRI), where experts manually create brain masks. This labor-intensive process heavily relies on the annotator's expertise, as automation faces challenges such as low tissu...
Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention.
PLoS computational biology
Aug 8, 2024
Human listeners have the ability to direct their attention to a single speaker in a multi-talker environment. The neural correlates of selective attention can be decoded from a single trial of electroencephalography (EEG) data. In this study, leverag...
Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it i...
A Cascaded Mutliresolution Ensemble Deep Learning Framework for Large Scale Alzheimer's Disease Detection Using Brain MRIs.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Alzheimer's is progressive and irreversible type of dementia, which causes degeneration and death of cells and their connections in the brain. AD worsens over time and greatly impacts patients' life and affects their important mental functions, inclu...
A Multi-Classification Accessment Framework for Reproducible Evaluation of Multimodal Learning in Alzheimer's Disease.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Multimodal learning is widely used in automated early diagnosis of Alzheimer's disease. However, the current studies are based on an assumption that different modalities can provide more complementary information to help classify the samples from the...
Synthesis of higher-B CEST Z-spectra from lower-B data via deep learning and singular value decomposition.
NMR in biomedicine
Aug 7, 2024
Chemical exchange saturation transfer (CEST) MRI at 3 T suffers from low specificity due to overlapping CEST effects from multiple metabolites, while higher field strengths (B) allow for better separation of Z-spectral "peaks," aiding signal interpre...
AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning.
Neuroinformatics
Aug 7, 2024
Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the ...
Contrastive machine learning reveals Parkinson's disease specific features associated with disease severity and progression.
Communications biology
Aug 7, 2024
Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to diverse neuroanatomical alterations. This study employs a contrastive deep learning approach to analyze Magnetic Resonance Imaging (MRI) data from 932 P...
Free access via computational cloud to deep learning-based EEG assessment in neonatal hypoxic-ischemic encephalopathy: revolutionary opportunities to overcome health disparities.
Pediatric research
Aug 6, 2024
In this issue of Pediatric Research, Kota et al. evaluate a novel monitoring visual trend using deep-learning - Brain State of the Newborn (BSN)- based EEG as a bedside marker for severity of the encephalopathy in 46 neonates with hypoxic-ischemic en...
Attention-Based MultiOffset Deep Learning Reconstruction of Chemical Exchange Saturation Transfer (AMO-CEST) MRI.
IEEE journal of biomedical and health informatics
Aug 6, 2024
One challenge of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is the long scan time due to multiple acquisitions of images at different saturation frequency offsets. k-space under-sampling strategy is commonly used to...
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