PURPOSE: To enhance glioma segmentation, a 3D-MRI intelligent glioma segmentation method based on deep learning is introduced. This method offers significant guidance for medical diagnosis, grading, and treatment strategy selection.
PURPOSE: Magnetic Resonance Imaging (MRI) based three-dimensional analysis of knee cartilage has evolved to become fully automatic. However, when implementing these measurements across multiple clinical centers, scanner variability becomes a critical...
FMRI, a non-invasive neuroimaging technique, can detect emotional brain activation patterns. It allows researchers to observe functional changes in the brain, making it a valuable tool for emotion recognition. For improved emotion recognition systems...
BACKGROUND: Accumulating evidence suggests that migraine patients present abnormal brain responses to salient sensory and emotional stimuli. However, it is still unclear whether this is a generalized or domain-specific phenomenon. Employing a well-va...
Approaches studying the dynamics of resting-state functional magnetic resonance imaging (rs-fMRI) activity often focus on time-resolved functional connectivity (tr-FC). While many tr-FC approaches have been proposed, most are linear approaches, e.g. ...
Neoadjuvant therapy (NAT) is the standard treatment strategy for MRI-defined cT4 rectal cancer. Predicting tumor regression can guide the resection plane to some extent. Here, we covered pre-treatment MRI imaging of 363 cT4 rectal cancer patients rec...
BACKGROUND: Conventional magnetic resonance imaging (MRI) protocols for lower back pain require multiple sequences and long acquisition times, challenging healthcare systems amid rising demand for lumbar spine imaging.
Using radio signals from a magnetic field, magnetic resonance imaging (MRI) represents a medical procedure that produces images to provide more information than typical scans. Diagnosing brain tumors from MRI is difficult because of the wide range of...
Prostate cancer (PCa) is one of the most common cancers among men, and artificial intelligence (AI) is emerging as a promising tool to enhance its diagnosis. This work proposes a classification approach for PCa cases using deep learning techniques. W...
OBJECTIVE: This meta-analysis evaluates the diagnostic accuracy of machine learning (ML)-based magnetic resonance imaging (MRI) models in distinguishing benign from malignant breast lesions and explores factors influencing their performance.
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