Radiology

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

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Artificial intelligence-based computer-aided diagnosis for breast cancer detection on digital mammography in Hong Kong.

INTRODUCTION: Research concerning artificial intelligence in breast cancer detection has primarily f...

a data augmentation strategy to narrow the robustness gap between expert radiologists and deep learning classifiers.

PURPOSE: Successful performance of deep learning models for medical image analysis is highly depende...

Generative Adversarial Network Based Contrast Enhancement: Synthetic Contrast Brain Magnetic Resonance Imaging.

RATIONALE AND OBJECTIVES: Magnetic resonance imaging (MRI) is a vital tool for diagnosing neurologic...

Quantitative analysis of deep learning reconstruction in CT angiography: Enhancing CNR and reducing dose.

BACKGROUND: Computed tomography angiography (CTA) provides significant information on image quality ...

Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques.

BACKGROUND: Inflammation of coronary arterial plaque is considered a key factor in the development o...

Intelligent imaging technology applications in multidisciplinary hospitals.

With the rapid development of artificial intelligence technology, its applications in medical imagin...

Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review.

Nuclear medicine imaging (NMI) is essential for the diagnosis and sensing of various diseases; howev...

Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.

BACKGROUND: The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose a...

Cost-effectiveness analysis of AI-based image quality control for perinatal ultrasound screening.

PURPOSE: This study aimed to compare the cost-effectiveness of AI-based approaches with manual appro...

Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection.

Ultrasound (US) imaging is an essential diagnostic technique in prenatal care, enabling enhanced sur...

Segmentation for mammography classification utilizing deep convolutional neural network.

BACKGROUND: Mammography for the diagnosis of early breast cancer (BC) relies heavily on the identifi...

Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images.

Recognition and segmentation of brain tumours (BT) using MR images are valuable and tedious processe...

Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings.

BACKGROUND: Deep learning (DL) often requires an image quality metric; however, widely used metrics ...

Attention-based Fusion Network for Breast Cancer Segmentation and Classification Using Multi-modal Ultrasound Images.

OBJECTIVE: Breast cancer is one of the most commonly occurring cancers in women. Thus, early detecti...

A deep learning method for total-body dynamic PET imaging with dual-time-window protocols.

PURPOSE: Prolonged scanning durations are one of the primary barriers to the widespread clinical ado...

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