Radiology

Multi-path decoder U-Net: A weakly trained real-time segmentation network for object detection and localization in ultrasound scans.

Detecting and localizing an anatomical structure of interest within the field of view of an ultrasou...

Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks.

Identifying the cerebral arterial branches is essential for undertaking a computational approach to ...

Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques.

Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor and the third cause of can...

Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning.

Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitatio...

Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images.

 Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate...

Deep learning based unpaired image-to-image translation applications for medical physics: a systematic review.

. There is a growing number of publications on the application of unpaired image-to-image (I2I) tran...

Artificial Contrast: Deep Learning for Reducing Gadolinium-Based Contrast Agents in Neuroradiology.

Deep learning approaches are playing an ever-increasing role throughout diagnostic medicine, especia...

New horizons in human sperm selection for assisted reproduction.

Male infertility is a commonly encountered pathology that is estimated to be a contributory factor i...

Editorial for "Deep Learning-Based Acceleration of Compressed Sensing Noncontrast-Enhanced Coronary Magnetic Resonance Angiography in Patients With Suspected Coronary Artery Disease".

Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.

With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative ...

Clinical applications of deep learning in breast MRI.

Deep learning (DL) is one of the most powerful data-driven machine-learning techniques in artificial...

An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study.

OBJECTIVE: Central lymph node metastasis (CLNM) is a predictor of poor prognosis for papillary thyro...

Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data.

OBJECTIVE: Investigate the performance of qualitative review (QR) for assessing dynamic susceptibili...

[Development of a planned and monitoring robotic assistance and automation for application in orthopedics and trauma surgery].

Robot-assisted surgery offers many advantages with respect to precision and facilitation in medicine...

Prospective intraindividual comparison of a standard 2D TSE MRI protocol for ankle imaging and a deep learning-based 2D TSE MRI protocol with a scan time reduction of 48.

PURPOSE: Magnetic resonance imaging (MRI) scan time remains a limited and valuable resource. This st...

Image quality comparison of lower extremity CTA between CT routine reconstruction algorithms and deep learning reconstruction.

BACKGROUND: To evaluate the image quality of lower extremity computed tomography angiography (CTA) w...

Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition.

This paper explored techniques for diagnosing breast cancer using deep learning based medical image ...

Contribution of Retzius-sparing robot-assisted radical prostatectomy to the mechanism of urinary continence as demonstrated by dynamic MRI.

Retzius-sparing robot-assisted radical prostatectomy (RARP) has been reported to exhibit better post...

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision.

Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (EC...

Enhancing Nodule Biopsy Through Technology Integration.

Technology in navigating to peripheral pulmonary nodules has improved in recent years. The recent in...

Segmentation of the aorta in systolic phase from 4D flow MRI: multi-atlas vs. deep learning.

OBJECTIVE: In the management of the aortic aneurysm, 4D flow magnetic resonance Imaging provides val...