AIMC Topic: Magnetic Resonance Imaging
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Magnetic Resonance Imaging Images under Deep Learning in the Identification of Tuberculosis and Pneumonia.
Journal of healthcare engineering
Dec 15, 2021
This work aimed to explore the application value of deep learning-based magnetic resonance imaging (MRI) images in the identification of tuberculosis and pneumonia, in order to provide a certain reference basis for clinical identification. In this st...
Spine Medical Image Segmentation Based on Deep Learning.
Journal of healthcare engineering
Dec 15, 2021
The aim was to further explore the clinical value of deep learning algorithm in the field of spinal medical image segmentation, and this study designed an improved U-shaped network (BN-U-Net) algorithm and applied it to the spinal MRI medical image s...
Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials.
Radiology
Dec 14, 2021
Background Deep learning-based segmentation could facilitate rapid and reproducible T1 lesion load assessments, which is crucial for disease management in multiple sclerosis (MS). T1 unenhancing and contrast-enhancing lesions in MS are those that enh...
A CNN-transformer hybrid approach for decoding visual neural activity into text.
Computer methods and programs in biomedicine
Dec 14, 2021
BACKGROUND AND OBJECTIVE: Most studies used neural activities evoked by linguistic stimuli such as phrases or sentences to decode the language structure. However, compared to linguistic stimuli, it is more common for the human brain to perceive the o...
A whole-process interpretable and multi-modal deep reinforcement learning for diagnosis and analysis of Alzheimer's disease.
Journal of neural engineering
Dec 13, 2021
. Alzheimer's disease (AD), a common disease of the elderly with unknown etiology, has been adversely affecting many people, especially with the aging of the population and the younger trend of this disease. Current artificial intelligence (AI) metho...
Automation of a Rule-based Workflow to Estimate Age from Brain MR Imaging of Infants and Children Up to 2 Years Old Using Stacked Deep Learning.
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Dec 10, 2021
PURPOSE: Myelination-related MR signal changes in white matter are helpful for assessing normal development in infants and children. A rule-based myelination evaluation workflow regarding signal changes on T1-weighted images (T1WIs) and T2-weighted i...
Deep-learning-assisted algorithm for catheter reconstruction during MR-only gynecological interstitial brachytherapy.
Journal of applied clinical medical physics
Dec 10, 2021
Magnetic resonance imaging (MRI) offers excellent soft-tissue contrast enabling the contouring of targets and organs at risk during gynecological interstitial brachytherapy procedure. Despite its advantage, one of the main obstacles preventing a tran...
Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based on Contrast-Enhanced Magnetic Resonance Imaging.
Journal of magnetic resonance imaging : JMRI
Dec 9, 2021
BACKGROUND: Misdiagnosis of malignant musculoskeletal tumors may lead to the delay of intervention, resulting in amputation or death.
Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography.
European journal of nuclear medicine and molecular imaging
Dec 9, 2021
PURPOSE: This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (μ) of the annihilation photons in PET.
Convolutional Neural Network With Graphical Lasso to Extract Sparse Topological Features for Brain Disease Classification.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2021
The functional connectivity provides new insights into the mechanisms of the human brain at network-level, which has been proved to be an effective biomarker for brain disease classification. Recently, machine learning methods have played an importan...
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