Radiology

Automated image label extraction from radiology reports - A review.

Machine Learning models need large amounts of annotated data for training. In the field of medical i...

Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress.

In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been de...

Human-scale navigation of magnetic microrobots in hepatic arteries.

Using external actuation sources to navigate untethered drug-eluting microrobots in the bloodstream ...

A machine-learning model based on dynamic contrast-enhanced MRI for preoperative differentiation between hepatocellular carcinoma and combined hepatocellular-cholangiocarcinoma.

AIM: To establish a machine-learning model based on dynamic contrast-enhanced (DCE) magnetic resonan...

Preliminary data on artificial intelligence tool in magnetic resonance imaging assessment of degenerative pathologies of lumbar spine.

PURPOSE: To evaluate the ability of an artificial intelligence (AI) tool in magnetic resonance imagi...

Deep learning-accelerated image reconstruction in back pain-MRI imaging: reduction of acquisition time and improvement of image quality.

INTRODUCTION: Low back pain is a global health issue causing disability and missed work days. Common...

Deep learning predicts prevalent and incident Parkinson's disease from UK Biobank fundus imaging.

Parkinson's disease is the world's fastest-growing neurological disorder. Research to elucidate the ...

Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan.

BACKGROUND: Peri-sinus structures such as arachnoid granulations (AG) and the parasagittal dural (PS...

Automated assessment of cardiac pathologies on cardiac MRI using T1-mapping and late gadolinium phase sensitive inversion recovery sequences with deep learning.

BACKGROUND: A deep learning (DL) model that automatically detects cardiac pathologies on cardiac MRI...

Optimizing High-Resolution MR Angiography: The Synergistic Effects of 3D Wheel Sampling and Deep Learning-Based Reconstruction.

OBJECTIVE: The aim of this study was to assess the utility of the combined use of 3D wheel sampling ...

Strong Diagnostic Performance of Single Energy 256-row Multidetector Computed Tomography with Deep Learning Image Reconstruction in the Assessment of Myocardial Fibrosis.

Objective Although magnetic resonance imaging (MRI) is the gold standard for evaluating abnormal myo...

Artificial intelligence (AI) in diagnostic imaging.

MRI-based prostate cancer classification using 3D efficient capsule network.

BACKGROUND: Prostate cancer (PCa) is the most common cancer in men and the second leading cause of m...

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning.

Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemod...

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparamet...

AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study.

INTRODUCTION: A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investi...

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification.

. The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assess...

Automated catheter segmentation and tip detection in cerebral angiography with topology-aware geometric deep learning.

BACKGROUND: Visual perception of catheters and guidewires on x-ray fluoroscopy is essential for neur...

Improving quantitative MRI using self-supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping.

PURPOSE: This paper proposes a novel self-supervised learning framework that uses model reinforcemen...

Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance.

BACKGROUND: The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonanc...

Knee landmarks detection via deep learning for automatic imaging evaluation of trochlear dysplasia and patellar height.

OBJECTIVES: To develop and validate a deep learning-based approach to automatically measure the pate...