AIMC Topic: Magnetic Resonance Imaging
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A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI.
Scientific reports
Apr 29, 2025
Parkinson's disease ranks as the second most prevalent neurological disorder after Alzheimer's disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson's disease (PD) detection using MR images. However, CNN models gen...
Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning.
European radiology experimental
Apr 29, 2025
BACKGROUND: T2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess image quality (IQ) on a patient-specific basis without radiologist oversight.
From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas.
Neurosurgical review
Apr 29, 2025
Gliomas are the most common primary tumors of the central nervous system, and advances in genetics and molecular medicine have significantly transformed their classification and treatment. This study aims to predict the IDH1 genotype in gliomas using...
Enhanced Brain Functional Interaction Following BCI-Guided Supernumerary Robotic Finger Training Based on Sixth-Finger Motor Imagery.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 29, 2025
Supernumerary robotic finger (SRF) has shown unique advantages in the field of motor augmentation and rehabilitation, while the development of brain computer interface (BCI) technology has provided the possibility for direct control of SRF. However, ...
Global-Local Feature Fusion Network Based on Nonlinear Spiking Neural Convolutional Model for MRI Brain Tumor Segmentation.
International journal of neural systems
Apr 28, 2025
Due to the differences in size, shape, and location of brain tumors, brain tumor segmentation differs greatly from that of other organs. The purpose of brain tumor segmentation is to accurately locate and segment tumors from MRI images to assist doct...
MED-NCA: Bio-inspired medical image segmentation.
Medical image analysis
Apr 28, 2025
The reliance on computationally intensive U-Net and Transformer architectures significantly limits their accessibility in low-resource environments, creating a technological divide that hinders global healthcare equity, especially in medical diagnost...
A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm.
Academic radiology
Apr 28, 2025
RATIONALE AND OBJECTIVES: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other n...
Deep learning-assisted detection of meniscus and anterior cruciate ligament combined tears in adult knee magnetic resonance imaging: a crossover study with arthroscopy correlation.
International orthopaedics
Apr 28, 2025
AIM: We aimed to compare the diagnostic performance of physicians in the detection of arthroscopically confirmed meniscus and anterior cruciate ligament (ACL) tears on knee magnetic resonance imaging (MRI), with and without assistance from a deep lea...
Gadoxetic acid-enhanced MRI for identifying cholangiocyte phenotype hepatocellular carcinoma by interpretable machine learning: individual application of SHAP.
BMC cancer
Apr 28, 2025
PURPOSE: Cholangiocyte phenotype hepatocellular carcinoma (HCC) is highly invasive. This study aims to develop and validate an optimal machine learning model to predict cholangiocyte phenotype HCC based on T1 mapping gadoxetic acid-enhanced MRI and t...
CUAMT: A MRI semi-supervised medical image segmentation framework based on contextual information and mixed uncertainty.
Computer methods and programs in biomedicine
Apr 27, 2025
BACKGROUND AND OBJECTIVE: Semi-supervised medical image segmentation is a class of machine learning paradigms for segmentation model training and inference using both labeled and unlabeled medical images, which can effectively reduce the data labelin...
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