AIMC Topic:
Magnetic Resonance Imaging
Clear Filters Showing 1311 to 1320 of 5975 articles
Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network.
Computers in biology and medicine
May 1, 2024
As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown path...
Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.
Cell reports. Medicine
May 1, 2024
Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of medical care for natural killer/T cell lymphoma (NKTCL). Artificial intelligence (AI)-based systems are developed based on nasopharynx magnetic resonan...
Associating Knee Osteoarthritis Progression with Temporal-Regional Graph Convolutional Network Analysis on MR Images.
Journal of magnetic resonance imaging : JMRI
Apr 30, 2024
BACKGROUND: Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability.
Letter to the Editor Regarding Article "Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset".
Journal of imaging informatics in medicine
Apr 30, 2024
The cited article reports on a convolutional neural network trained to predict response to neoadjuvant chemotherapy from pre-treatment breast MRI scans. The proposed algorithm attains impressive performance on the test dataset with a mean Area Under ...
An Optimization Numerical Spiking Neural Membrane System with Adaptive Multi-Mutation Operators for Brain Tumor Segmentation.
International journal of neural systems
Apr 30, 2024
Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or skull artifacts. Therefore, MRI images are widely used to achieve the segmentation of brain tumors. ...
Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases.
Journal of neuro-oncology
Apr 30, 2024
OBJECTIVE: Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differe...
Assessment of the deep learning-based gamma passing rate prediction system for 1.5 T magnetic resonance-guided linear accelerator.
Radiological physics and technology
Apr 30, 2024
Measurement-based verification is impossible for the patient-specific quality assurance (QA) of online adaptive magnetic resonance imaging-guided radiotherapy (oMRgRT) because the patient remains on the couch throughout the session. We assessed a dee...
Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering.
BMC medical informatics and decision making
Apr 30, 2024
Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter ...
Study on the differential diagnosis of benign and malignant breast lesions using a deep learning model based on multimodal images.
Journal of cancer research and therapeutics
Apr 30, 2024
OBJECTIVE: To establish a multimodal model for distinguishing benign and malignant breast lesions.
Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network.
Neural plasticity
Apr 30, 2024
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms underlying ADHD remain inadequately understood, and current approaches...
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