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Magnetic Resonance Imaging

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RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.

Biomedical physics & engineering express
Accurate identification of molecular subtypes in breast cancer is critical for personalized treatment. This study introduces a novel neural network model, RAE-Net, based on Multimodal Feature Fusion (MFF) and the Evidential Deep Learning Algorithm (E...

Deep transfer learning radiomics for distinguishing sinonasal malignancies: a preliminary MRI study.

Future oncology (London, England)
PURPOSE: This study aimed to assess the diagnostic accuracy of combining MRI hand-crafted (HC) radiomics features with deep transfer learning (DTL) in identifying sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC), and non-Hodgki...

Deep learning to quantify the pace of brain aging in relation to neurocognitive changes.

Proceedings of the National Academy of Sciences of the United States of America
Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since bi...

AI for image quality and patient safety in CT and MRI.

European radiology experimental
Substantial endeavors have been recently dedicated to developing artificial intelligence (AI) solutions, especially deep learning-based, tailored to enhance radiological procedures, in particular algorithms designed to minimize radiation exposure and...

TTGA U-Net: Two-stage two-stream graph attention U-Net for hepatic vessel connectivity enhancement.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate segmentation of hepatic vessels is pivotal for guiding preoperative planning in ablation surgery utilizing CT images. While non-contrast CT images often lack observable vessels, we focus on segmenting hepatic vessels within preoperative MR i...

MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging.

Medical image analysis
Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a specific sub-region, multi-view learning captures more information by analyzing multiple patches sim...

Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts.

Tomography (Ann Arbor, Mich.)
BACKGROUND/OBJECTIVE: Longitudinal in vivo studies of murine xenograft models are widely utilized in oncology to study cancer biology and develop therapies. Magnetic resonance imaging (MRI) of these tumors is an invaluable tool for monitoring tumor g...

Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.

Medical image analysis
The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Records (EHRs) is a crucial aspect of modern healthcare. Integrative analysis of multiple sources can provide a com...

Incorporating indirect MRI information in a CT-based deep learning model for prostate auto-segmentation.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Computed tomography (CT) imaging poses challenges for delineation of soft tissue structures for prostate cancer external beam radiotherapy. Guidelines require the input of magnetic resonance imaging (MRI) information. We devel...

Multi-feature fusion method combining brain functional connectivity and graph theory for schizophrenia classification and neuroimaging markers screening.

Journal of psychiatric research
BACKGROUND: The abnormalities in brain functional connectivity (FC) and graph topology (GT) in patients with schizophrenia (SZ) are unclear. Researchers proposed machine learning algorithms by combining FC or GT to identify SZ from healthy controls. ...