AIMC Topic: Magnetic Resonance Imaging
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Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network.
Microscopy research and technique
Dec 20, 2024
The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, inte...
Diagnostic accuracy of texture analysis applied to T- and T-Relaxation maps for liver fibrosis classification via machine-learning algorithms with liver histology as reference standard.
European journal of radiology
Dec 20, 2024
OBJECTIVES: To explore texture analysis' ability on T and T relaxation maps to classify liver fibrosis into no-to-mild liver fibrosis (nmF) versus severe fibrosis (sF) group using machine learning algorithms and histology as reference standard.
Area Under Curve
Biopsy
Image Interpretation, Computer-Assisted
Reference Standards
Elasticity Imaging Techniques
Support Vector Machine
Female
Echo-Planar Imaging
Middle Aged
Magnetic Resonance Imaging
Aged
Severity of Illness Index
Liver
Adult
ROC Curve
Humans
Male
Principal Component Analysis
Liver Cirrhosis
Retrospective Studies
Machine Learning Recognizes Stages of Parkinson's Disease Using Magnetic Resonance Imaging.
Sensors (Basel, Switzerland)
Dec 20, 2024
Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early ...
Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques.
BMC medical imaging
Dec 20, 2024
INTRODUCTION: Gadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-a...
Evaluation of a deep learning prostate cancer detection system on biparametric MRI against radiological reading.
European radiology
Dec 19, 2024
OBJECTIVES: This study aims to evaluate a deep learning pipeline for detecting clinically significant prostate cancer (csPCa), defined as Gleason Grade Group (GGG) ≥ 2, using biparametric MRI (bpMRI) and compare its performance with radiological read...
Deep learning radiomics nomograms predict Isocitrate dehydrogenase (IDH) genotypes in brain glioma: A multicenter study.
Magnetic resonance imaging
Dec 19, 2024
PURPOSE: To explore the feasibility of Deep learning radiomics nomograms (DLRN) in predicting IDH genotype.
Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen.
Medical physics
Dec 19, 2024
BACKGROUND: Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since the...
Image quality assessment and white matter hyperintensity quantification in two accelerated high-resolution 3D FLAIR techniques: Wave-CAIPI and deep learning-based SPACE.
Clinical radiology
Dec 19, 2024
AIM: To compare the image quality obtained using two accelerated high-resolution 3D fluid-attenuated inversion recovery (FLAIR) techniques for the brain-deep learning-reconstruction SPACE (DL-SPACE) and Wave-CAIPI FLAIR.
Deep learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma and idiopathic orbital inflammation.
European radiology
Dec 19, 2024
OBJECTIVES: To evaluate the value of deep-learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI).
Generating 3D brain tumor regions in MRI using vector-quantization Generative Adversarial Networks.
Computers in biology and medicine
Dec 19, 2024
Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The commo...
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