Radiology

Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorder using a deep learning model.

Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are autoimmune inflammato...

Robotic ultrasound imaging: State-of-the-art and future perspectives.

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis du...

MRI-based deep learning model for differentiation of hepatic hemangioma and hepatoblastoma in early infancy.

UNLABELLED: Hepatic hemangioma (HH) and hepatoblastoma (HBL) are common pediatric liver tumors and p...

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging.

PURPOSE OF REVIEW: Early and accurate diagnosis of pancreatic cancer is crucial for improving patien...

The Role of Organized Radiology in Advancing Imaging Artificial Intelligence.

Performance of deep learning-based autodetection of arterial stenosis on head and neck CT angiography: an independent external validation study.

PURPOSE: To externally validate the performance of automated stenosis detection on head and neck CT ...

Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept.

The patient setup technique currently in practice in most radiotherapy departments utilises on-couch...

Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis.

BACKGROUND: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopte...

Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography Based on ResAttU-Net for Transcranial Brain Hemorrhage Detection.

OBJECTIVE: Hemorrhagic stroke is a leading threat to human's health. The fast-developing microwave-i...

Comparative verification of control methodology for robotic interventional neuroradiology procedures.

PURPOSE: The use of robotics is emerging for performing interventional radiology procedures. Robots ...

Auto-Segmentation and Classification of Glioma Tumors with the Goals of Treatment Response Assessment Using Deep Learning Based on Magnetic Resonance Imaging.

Glioma is the most common primary intracranial neoplasm in adults. Radiotherapy is a treatment appro...

Deep learning-based prediction of H3K27M alteration in diffuse midline gliomas based on whole-brain MRI.

BACKGROUND: H3K27M mutation status significantly affects the prognosis of patients with diffuse midl...

Phasor Representation Approach for Rapid Exploratory Analysis of Large Infrared Spectroscopic Imaging Data Sets.

Infrared (IR) spectroscopic imaging is potentially useful for digital histopathology as it provides ...

Unsupervised abnormality detection in neonatal MRI brain scans using deep learning.

Analysis of 3D medical imaging data has been a large topic of focus in the area of Machine Learning/...

Understanding Biases and Disparities in Radiology AI Datasets: A Review.

Artificial intelligence (AI) continues to show great potential in disease detection and diagnosis on...

Deep-learning visualization enhancement method for optical coherence tomography angiography in dermatology.

Optical coherence tomography angiography (OCTA) in dermatology usually suffers from low image qualit...

Deep learning for artery-vein classification in optical coherence tomography angiography.

Major retinopathies can differentially impact the arteries and veins. Traditional fundus photography...

An unsupervised two-step training framework for low-dose computed tomography denoising.

BACKGROUND: Although low-dose computed tomography (CT) imaging has been more widely adopted in clini...

Deep-learning based segmentation of ultrasound adipose image for liposuction.

BACKGROUND: To develop an automatic and reliable ultrasonic visual system for robot- or computer-ass...

Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining.

We present a novel computer algorithm to automatically detect and segment pulmonary embolisms (PEs) ...

Unraveling the complexity of Optical Coherence Tomography image segmentation using machine and deep learning techniques: A review.

Optical Coherence Tomography (OCT) is an emerging technology that provides three-dimensional images ...