Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 4, 2025
In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parcellation. The key innovation of DDEvENet is the design of an evidential deep learning framework to q...
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Brain functional network (BFN) analysis has become a popular method for identifying neurological diseases at their early stages and revealing sensitive biomarkers related to these diseases. Due to the fact that BFN is a graph with complex structure, ...
Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive imaging technique to study patterns of brain activity, and is increasingly used to facilitate automated brain disorder analysis. Existing fMRI-based learning method...
OBJECTIVES: In the context of growing safety concerns regarding the use of gadolinium-based contrast agents in contrast-enhanced MRI, there is a need for dose reduction without compromising diagnostic accuracy. A deep learning (DL) method is proposed...
IEEE transactions on neural networks and learning systems
Oct 29, 2024
Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnosis of brain disorders. However, state-of-the-art studies used to build the FCN using a single brain parcellation atlas at...
In brain network analysis, individual-level data can provide biological features of individuals, while population-level data can provide demographic information of populations. However, existing methods mostly utilize either individual- or population...
AJNR. American journal of neuroradiology
Sep 9, 2024
BACKGROUND AND PURPOSE: Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizabil...
Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as they offer additional valuable insights by establishing connections between EEG signal channels. Whi...
PURPOSE: To assess image quality and diagnostic confidence of 3D T1-weighted spoiled gradient echo (SPGR) MRI using artificial intelligence (AI) reconstruction.
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