Radiology

Artificial neural network for enhancing signal-to-noise ratio and contrast in photothermal optical coherence tomography.

Optical coherence tomography (OCT) is a medical imaging method that generates micron-resolution 3D v...

TransEBUS: The interpretation of endobronchial ultrasound image using hybrid transformer for differentiating malignant and benign mediastinal lesions.

The purpose of this study is to establish a deep learning automatic assistance diagnosis system for ...

Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics (DLR) model based on con...

Decoding medical jargon: The use of AI language models (ChatGPT-4, BARD, microsoft copilot) in radiology reports.

OBJECTIVE: Evaluate Artificial Intelligence (AI) language models (ChatGPT-4, BARD, Microsoft Copilot...

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.

G2ViT: Graph Neural Network-Guided Vision Transformer Enhanced Network for retinal vessel and coronary angiograph segmentation.

Blood vessel segmentation is a crucial stage in extracting morphological characteristics of vessels ...

PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.

PURPOSE: High PSMA expression might be correlated with structural characteristics such as growth pat...

Prediction of endovascular leaks after thoracic endovascular aneurysm repair though machine learning applied to pre-procedural computed tomography angiographs.

To predict endoleaks after thoracic endovascular aneurysm repair (TEVAR) we submitted patient charac...

BraNet: a mobil application for breast image classification based on deep learning algorithms.

Mobile health apps are widely used for breast cancer detection using artificial intelligence algorit...

Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers.

We aimed to develop a new artificial intelligence software that can automatically extract and measur...

PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head.

During the process of labor, the intrapartum transperineal ultrasound examination serves as a valuab...

Artificial intelligence in interventional radiology: state of the art.

Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in i...

Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI.

Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the st...

Semi-Supervised Thyroid Nodule Detection in Ultrasound Videos.

Deep learning techniques have been investigated for the computer-aided diagnosis of thyroid nodules ...

Attentional adversarial training for few-shot medical image segmentation without annotations.

Medical image segmentation is a critical application that plays a significant role in clinical resea...

Deep Learning Synthesis of White-Blood From Dark-Blood Late Gadolinium Enhancement Cardiac Magnetic Resonance.

OBJECTIVES: Dark-blood late gadolinium enhancement (DB-LGE) cardiac magnetic resonance has been prop...

Predicting Osteoporosis and Osteopenia by Fusing Deep Transfer Learning Features and Classical Radiomics Features Based on Single-Source Dual-energy CT Imaging.

RATIONALE AND OBJECTIVES: To develop and validate a predictive model for osteoporosis and osteopenia...

Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network.

As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple...

Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.

Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of m...

Artificial intelligence-based classification of breast lesion from contrast enhanced mammography: a multicenter study.

PURPOSE: The authors aimed to establish an artificial intelligence (AI)-based method for preoperativ...

Impact of F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.

We aimed to investigate the effects of F-FDG PET voxel intensity normalization on radiomic features ...