AIMC Topic: Magnetic Resonance Imaging

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Enhancing brain tumor classification by integrating radiomics and deep learning features: A comprehensive study utilizing ensemble methods on MRI scans.

Journal of X-ray science and technology Dec 9, 2024
BACKGROUND AND OBJECTIVE: This study aims to assess the effectiveness of combining radiomics features (RFs) with deep learning features (DFs) for classifying brain tumors-specifically Glioma, Meningioma, and Pituitary Tumor-using MRI scans and advanc...
Magnetic Resonance Imaging Brain Neoplasms Male Middle Aged Humans Neural Networks, Computer Brain Deep Learning Image Processing, Computer-Assisted Glioma Female Support Vector Machine Adult Radiomics
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Automated Segmentation of MRI White Matter Hyperintensities in 8421 Patients with Acute Ischemic Stroke.

AJNR. American journal of neuroradiology Dec 9, 2024
BACKGROUND AND PURPOSE: To date, only a few small studies have attempted deep learning-based automatic segmentation of white matter hyperintensity (WMH) lesions in patients with cerebral infarction; this issue is complicated because stroke-related le...
Republic of Korea Female Reproducibility of Results Aged Humans Algorithms Male Image Interpretation, Computer-Assisted Middle Aged White Matter Magnetic Resonance Imaging Deep Learning Ischemic Stroke
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Development and Validation of a Deep Learning System to Differentiate HER2-Zero, HER2-Low, and HER2-Positive Breast Cancer Based on Dynamic Contrast-Enhanced MRI.

Journal of magnetic resonance imaging : JMRI Dec 6, 2024
BACKGROUND: Previous studies explored MRI-based radiomic features for differentiating between human epidermal growth factor receptor 2 (HER2)-zero, HER2-low, and HER2-positive breast cancer, but deep learning's effectiveness is uncertain.
Retrospective Studies Female Humans Sensitivity and Specificity ROC Curve Reproducibility of Results Breast Neoplasms Contrast Media Middle Aged Aged Magnetic Resonance Imaging Receptor, ErbB-2 Breast Deep Learning Adult Image Interpretation, Computer-Assisted
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Estimation of fatty acid composition in mammary adipose tissue using deep neural network with unsupervised training.

Magnetic resonance in medicine Dec 6, 2024
PURPOSE: To develop a deep learning-based method for robust and rapid estimation of the fatty acid composition (FAC) in mammary adipose tissue.
Fatty Acids Phantoms, Imaging Unsupervised Machine Learning Adipose Tissue Breast Neural Networks, Computer Female Breast Neoplasms Image Processing, Computer-Assisted Magnetic Resonance Imaging Middle Aged Deep Learning Humans
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Automated Classification of Body MRI Sequences Using Convolutional Neural Networks.

Academic radiology Dec 6, 2024
RATIONALE AND OBJECTIVES: Multi-parametric MRI (mpMRI) studies of the body are routinely acquired in clinical practice. However, a standardized naming convention for MRI protocols and series does not exist currently. Conflicts in the series descripti...
Convolutional Neural Networks Pilot Projects Abdomen Multiparametric Magnetic Resonance Imaging Magnetic Resonance Imaging Pelvis Image Interpretation, Computer-Assisted Humans Thorax Neural Networks, Computer
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Magnetic resonance image denoising for Rician noise using a novel hybrid transformer-CNN network (HTC-net) and self-supervised pretraining.

Medical physics Dec 6, 2024
BACKGROUND: Magnetic resonance imaging (MRI) is a crucial technique for both scientific research and clinical diagnosis. However, noise generated during MR data acquisition degrades image quality, particularly in hyperpolarized (HP) gas MRI. While de...
Magnetic Resonance Imaging Signal-To-Noise Ratio Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted Humans
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A multi-view learning approach with diffusion model to synthesize FDG PET from MRI T1WI for diagnosis of Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association Dec 6, 2024
INTRODUCTION: This study presents a novel multi-view learning approach for machine learning (ML)-based Alzheimer's disease (AD) diagnosis.
Humans Machine Learning Alzheimer Disease Positron-Emission Tomography Fluorodeoxyglucose F18 Brain Magnetic Resonance Imaging Radiopharmaceuticals
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Automatic segmentation of pericardial adipose tissue from cardiac MR images via semi-supervised method with difference-guided consistency.

Medical physics Dec 5, 2024
BACKGROUND: Accurate and automatic segmentation of pericardial adipose tissue (PEAT) in cardiac magnetic resonance (MR) images is essential for the diagnosis and treatment of cardiovascular diseases. Precise segmentation is challenging due to high co...
Humans Heart Supervised Machine Learning Automation Image Processing, Computer-Assisted Pericardium Magnetic Resonance Imaging Adipose Tissue
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A prognostic and predictive model based on deep learning to identify optimal candidates for intensity-modulated radiotherapy alone in patients with stage II nasopharyngeal carcinoma: A retrospective multicenter study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Dec 5, 2024
PURPOSE: To develop and validate a prognostic and predictive model integrating deep learning MRI features and clinical information in patients with stage II nasopharyngeal carcinoma (NPC) to identify patients with a low risk of progression for whom i...
Neoplasm Staging Radiotherapy, Intensity-Modulated Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Male Middle Aged Humans Deep Learning Magnetic Resonance Imaging Prognosis Female Retrospective Studies Aged Adult
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Personalized federated learning for abdominal multi-organ segmentation based on frequency domain aggregation.

Journal of applied clinical medical physics Dec 5, 2024
PURPOSE: The training of deep learning (DL) models in medical images requires large amounts of sensitive patient data. However, acquiring adequately labeled datasets is challenging because of the heavy workload of manual annotations and the stringent...
Federated Learning Humans Algorithms Tomography, X-Ray Computed Abdomen Magnetic Resonance Imaging Deep Learning Image Processing, Computer-Assisted
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