Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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Deep Learning-Enhanced Ultra-high-resolution CT Imaging for Superior Temporal Bone Visualization.

RATIONALE AND OBJECTIVES: This study assesses the image quality of temporal bone ultra-high-resoluti...

Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?

The advances in artificial intelligence (AI) technology in recent years have been remarkable, and th...

CECRel: A joint entity and relation extraction model for Chinese electronic medical records of coronary angiography via contrastive learning.

Entity and relation extraction from Chinese electronic medical records (EMRs) is a crucial foundatio...

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

PURPOSE: This study aimed to assess the diagnostic accuracy of combining MRI hand-crafted (HC) radio...

Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement From the American Heart Association.

Artificial intelligence is poised to transform cardio-oncology by enabling personalized care for pat...

Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning.

Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motio...

An enhanced denoising system for mammogram images using deep transformer model with fusion of local and global features.

Image denoising is a critical problem in low-level computer vision, where the aim is to reconstruct ...

Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging.

Oral carcinoma (OC) is a toxic illness among the most general malignant cancers globally, and it has...

[MP-MRI in the evaluation of non-operative treatment response, for residual and recurrent tumor detection in head and neck cancer].

As non-surgical therapies gain acceptance in head and neck tumors, the importance of imaging has inc...

Intraoperative stenosis detection in X-ray coronary angiography via temporal fusion and attention-based CNN.

BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD), the leading cause of mortality, is caused b...

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

Substantial endeavors have been recently dedicated to developing artificial intelligence (AI) soluti...

CT Imaging of the Pancreas: A Review of Current Developments and Applications.

Pancreatic cancer continues to pose daily challenges to clinicians, radiologists, and researchers. T...

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

Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical prac...

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

BACKGROUND/OBJECTIVE: Longitudinal in vivo studies of murine xenograft models are widely utilized in...

A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.

Chest computed tomography (CT) scans are essential for accurately assessing the severity of the nove...

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

The integration of diverse clinical modalities such as medical imaging and the tabular data extracte...

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

BACKGROUND AND PURPOSE: Computed tomography (CT) imaging poses challenges for delineation of soft ti...

Machine Learning in Intravascular Ultrasound: Validating Automated Lesion Assessment for Complex Coronary Interventions.

BACKGROUND: Intravascular ultrasound (IVUS) is essential for assessing complex coronary lesions, but...

Deep learning models for differentiating three sinonasal malignancies using multi-sequence MRI.

PURPOSE: To develop MRI-based deep learning (DL) models for distinguishing sinonasal squamous cell c...

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