Publications

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

The purpose of this retrospective study was to develop and evaluate the performance of U-Net to determine whether U-Net-based deep learning could accurately...

European Radiology

4 days ago

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images

In this study, the authors aimed to develop a deep learning-based artificial intelligence (AI) scheme in order to predict the likelihood of the ground-glass...

European Radiology

3 weeks ago

Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach

The aim of this study was to evaluate whether machine learning algorithms allow for the prediction of Child-Pugh classification on clinical multiphase...

European Radiology Experimental

4 weeks ago

Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT

The authors of this study aimed to determine the diagnostic performance of a deep learning algorithm for the automated detection of small 18F-FDG-avid...

European Radiology

1 month ago

Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey

Our study surveyed 484 medical students from 17 medical schools on their views towards AI. We also explored the current state of AI education in the UK. The...

Daniel Poon

2 months ago

Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph

The authors of this retrospective study performed test-retest reproducibility analyses for a deep learning-based automatic detection algorithm (DLAD) using...

European Radiology

2 months ago

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound

The aim of this study was to establish and validate an artificial intelligence-based radiomics strategy in order to predict personalized responses to...

European Radiology

2 months ago

Deep learning workflow in radiology: how to get started

In the past decade, deep learning architectures, which essentially consist of neural networks with numerous layers, have emerged as a dominant class of...

An Tang

2 months ago

Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis

Multiple automated methods for segmentation of multiple sclerosis (MS) lesions have been developed over the past years, and the use of artificial neural...

Philipp Kickingereder

2 months ago

Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer

In this study, the authors aimed to build a dual-energy CT (DECT)-based deep learning radiomics nomogram that could be used for lymph node metastasis...

European Radiology

3 months ago