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Magnetic Resonance Imaging

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Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.

Computers in biology and medicine
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmenta...

Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.

JCO clinical cancer informatics
PURPOSE: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evalu...

Application of elastic net for clinical outcome prediction and classification in progressive supranuclear palsy: A multicenter cohort study.

Parkinsonism & related disorders
BACKGROUND: Previous studies have used machine learning to identify clinically relevant atrophic regions in progressive supranuclear palsy (PSP). This study applied Elastic Net (EN) in PSP to uncover key atrophic patterns, offering a novel approach t...

Assessment of glymphatic function and white matter integrity in children with autism using multi-parametric MRI and machine learning.

European radiology
OBJECTIVES: To assess glymphatic function and white matter integrity in children with autism spectrum disorder (ASD) using multi-parametric MRI, combined with machine learning to evaluate ASD detection performance.

A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography.

NeuroImage
Magnetic resonance electrical properties tomography can extract the electrical properties of in-vivo tissue. To estimate tissue electrical properties, various reconstruction algorithms have been proposed. However, physics-based reconstructions are pr...

Enhanced detection of mild cognitive impairment in Alzheimer's disease: a hybrid model integrating dual biomarkers and advanced machine learning.

BMC geriatrics
Alzheimer's disease (AD) is a complex, progressive, and irreversible neurodegenerative disorder marked by cognitive decline and memory loss. Early diagnosis is the most effective strategy to slow the disease's progression. Mild Cognitive Impairment (...

MR-linac: role of artificial intelligence and automation.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
The integration of artificial intelligence (AI) into radiotherapy has advanced significantly during the past 5 years, especially in terms of automating key processes like organ at risk delineation and treatment planning. These innovations have enhanc...

A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity.

Scientific reports
Functional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connectomes, combined with small sample sizes in clinical research, increases the risk of overfitting. Rece...

CDCG-UNet: Chaotic Optimization Assisted Brain Tumor Segmentation Based on Dilated Channel Gate Attention U-Net Model.

Neuroinformatics
Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existin...

Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis.

Biomedical physics & engineering express
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing b...