AIMC Topic: Magnetic Resonance Imaging

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GNN-based structural information to improve DNN-based basal ganglia segmentation in children following early brain lesion.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society May 7, 2024
Analyzing the basal ganglia following an early brain lesion is crucial due to their noteworthy role in sensory-motor functions. However, the segmentation of these subcortical structures on MRI is challenging in children and is further complicated by ...
Basal Ganglia Image Processing, Computer-Assisted Magnetic Resonance Imaging Ischemic Stroke Deep Learning Child, Preschool Child Humans Neural Networks, Computer Infant, Newborn Infant
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DenseNet model incorporating hybrid attention mechanisms and clinical features for pancreatic cystic tumor classification.

Journal of applied clinical medical physics May 7, 2024
PURPOSE: The aim of this study is to develop a deep learning model capable of discriminating between pancreatic plasma cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) by leveraging patient-specific clinical features and imaging outcomes. T...
Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Pancreatic Cyst Humans Pancreatic Neoplasms Algorithms Image Interpretation, Computer-Assisted Female Male
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Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning.

Biological psychiatry May 6, 2024
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better un...
Humans Machine Learning Magnetic Resonance Imaging Neurodegenerative Diseases Mental Disorders Brain Neuroimaging Endophenotypes
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3D-DGGAN: A Data-Guided Generative Adversarial Network for High Fidelity in Medical Image Generation.

IEEE journal of biomedical and health informatics May 6, 2024
Three-dimensional images are frequently used in medical imaging research for classification, segmentation, and detection. However, the limited availability of 3D images hinders research progress due to network training difficulties. Generative method...
Imaging, Three-Dimensional Magnetic Resonance Imaging Algorithms Humans Tomography, X-Ray Computed Neural Networks, Computer
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Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis.

IEEE journal of biomedical and health informatics May 6, 2024
Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, e...
Neural Networks, Computer Algorithms Brain Male Female Humans Magnetic Resonance Imaging Depressive Disorder, Major Young Adult Image Interpretation, Computer-Assisted Adult
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Bias-reduced neural networks for parameter estimation in quantitative MRI.

Magnetic resonance in medicine May 4, 2024
PURPOSE: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound.
Humans Neural Networks, Computer Bias Least-Squares Analysis Neuroimaging Reproducibility of Results Image Processing, Computer-Assisted Magnetic Resonance Imaging Algorithms Brain Computer Simulation
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A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn's disease.

Abdominal radiology (New York) May 4, 2024
OBJECTIVES: Differentiating intestinal tuberculosis (ITB) from Crohn's disease (CD) remains a diagnostic dilemma. Misdiagnosis carries potential grave implications. We aim to establish a multidisciplinary-based model using machine learning approach f...
Adult Male Middle Aged Machine Learning Retrospective Studies Female Crohn Disease Tuberculosis, Gastrointestinal Colonoscopy Magnetic Resonance Imaging Diagnosis, Differential Humans
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The Contribution of Explainable Machine Learning Algorithms Using ROI-based Brain Surface Morphology Parameters in Distinguishing Early-onset Schizophrenia From Bipolar Disorder.

Academic radiology May 3, 2024
RATIONALE AND OBJECTIVES: To differentiate early-onset schizophrenia (EOS) from early-onset bipolar disorder (EBD) using surface-based morphometry measurements and brain volumes using machine learning (ML) algorithms.
Bipolar Disorder Magnetic Resonance Imaging Diagnosis, Differential Schizophrenia Image Interpretation, Computer-Assisted Female Male Adolescent Machine Learning Algorithms Brain Humans
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Letter to the editor: Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue.

Oral surgery, oral medicine, oral pathology and oral radiology May 3, 2024
Humans Predictive Value of Tests Deep Learning Carcinoma, Squamous Cell Tongue Neoplasms Lymphatic Metastasis Radiomics Magnetic Resonance Imaging
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Radiomics model and deep learning model based on T1WI image for acute lymphoblastic leukemia identification.

Clinical radiology May 3, 2024
AIM: This study aimed to develop highly precise radiomics and deep learning models to accurately detect acute lymphoblastic leukemia (ALL) using a T1WI image.
Adolescent Child, Preschool Child Radiomics Humans Male Retrospective Studies Deep Learning Magnetic Resonance Imaging Female Image Interpretation, Computer-Assisted Sensitivity and Specificity Precursor Cell Lymphoblastic Leukemia-Lymphoma
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