machine learning

Deep learning: definition and perspectives for thoracic imaging

The authors of this review aimed to provide definitions for understanding the methods of machine learning, deep learning, and convolutional neural networks...

European Radiology

2 years 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

2 years ago

Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma

This study identified 236 patients from two cohorts who underwent surgery for ground-glass nodules (GGNs). The novel marginal features described, when...

European Radiology

2 years ago

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI

This preliminary study aimed to differentiate malignant from benign enhancing foci on breast MRI using radiomic signature. Forty-five patients were included...

European Radiology Experimental

2 years ago

Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs

In this study, the authors developed a deep feature fusion model (DFFM) in order to segment postoperative gliomas on CT images, which were guided by...

European Radiology

2 years ago

Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

In this work, we aimed to evaluate the potential value of the machine learning (ML)–based MRI texture analysis for predicting 1p/19q codeletion status of...

Burak Kocak

2 years ago

Reasons to do AI with Friends: The one where we tackle fibrosis

In the second episode of ESR Connect’s weekly show on artificial intelligence, Reasons to do AI with Friends, Prof. Helmut Prosch, from the Medical University...

ESR

2 years ago

Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

The aim of this retrospective study was to develop non-invasive machine learning (ML) classifiers for predicting post-Glenn shunt patients with low and high...

European Radiology

2 years ago

Reasons to do AI with Friends: The one where we classify breast cancer with MRI

“This was just awesome!” “Fantastic!” “Thank you for this great session!” These were only a few of the feedback messages...

ESR

2 years ago

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

The purpose of this study was to create a radiomics approach based on multiparametric MRI (mpMRI) features that were extracted from an auto-fixed volume of...

ESR

3 years ago