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classification

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.

Most used hashtags:

machine learningdeep learningradiomicsmrix-ray computed tomographyai newsCOVID-19interviewmammographybreast cancerneural networksdiagnostic imagingtomographyMDCTradiographyworkflow

Latest posts

Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI

Zheng-Han Yang February 2022

We investigated a saliency-based 3D convolutional neural network (CNN) to classify seven categories of common focal liver lesions and validated the model performance. This retrospective study included 557 lesions examined by multisequence MRI. We found that this interpretable deep learning model showed high diagnostic performance in the differentiation of common liver masses on multisequence MRI. A few important notes on

Read More →

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

European Radiology December 2021

This retrospective, multi-institutional study investigated machine learning classifiers and interpretable models using chest CT for the detection of COVID-19 and to differentiate this from types of pneumonia, interstitial lung disease (ILD), and normal CTs. The study included 2,446 chest CTs from across 16 different institutions and the authors’ method was found to accurately differentiate COVID-19 from other types of pneumonia,

Read More →

Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists

European Radiology November 2021

The authors of this retrospective study aimed to evaluate the diagnostic performance of a radiomics model in order to classify hepatic cyst, hemangioma, and metastasis in patients who have been diagnosed with colorectal cancer (CRC) from portal-phase abdominopelvic CT images. The study found that, although inferior to radiologists, the radiomics model was able to achieve substantial diagnostic performance when differentiating

Read More →

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

European Radiology March 2020

This study identified 236 patients from two cohorts who underwent surgery for ground-glass nodules (GGNs). The novel marginal features described, when combined with a radiomics model, could help to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) on preoperative CT scans. Key points Our novel marginal features could improve the existing radiomics model to

Read More →

Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification

European Radiology February 2020

The authors of this study evaluated the impact of utilizing digital breast tomosynthesis (DBT) and/or full-field digital mammography (FFDM), and different transfer learning strategies, on deep convolutional neural network (DCNN)-based mass classification for breast cancer. The authors found that integrating DBT and FFDM in DCNN training helps to enhance breast mass classification accuracy. Key points Transfer learning facilitates mass classification

Read More →

Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings

European Radiology January 2020

In this study, the authors sought to investigate the feasibility of a deep learning-based detection (DLD) system for multiclass legions on chest radiograph. The study included almost 16,000 chest radiographs collected from two tertiary hospitals, which were then used to develop a DLD system. The authors found that the DLD system showed potential for detections of lesions and pattern classification.

Read More →

Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI

Zheng-Han Yang February 2022

We investigated a saliency-based 3D convolutional neural network (CNN) to classify seven categories of common focal liver lesions and validated the model performance. This retrospective study included 557 lesions examined by multisequence MRI. We found that this interpretable deep learning model showed high diagnostic performance in the differentiation of common liver masses on multisequence MRI. A few important notes on

Read More →

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

European Radiology December 2021

This retrospective, multi-institutional study investigated machine learning classifiers and interpretable models using chest CT for the detection of COVID-19 and to differentiate this from types of pneumonia, interstitial lung disease (ILD), and normal CTs. The study included 2,446 chest CTs from across 16 different institutions and the authors’ method was found to accurately differentiate COVID-19 from other types of pneumonia,

Read More →

Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists

European Radiology November 2021

The authors of this retrospective study aimed to evaluate the diagnostic performance of a radiomics model in order to classify hepatic cyst, hemangioma, and metastasis in patients who have been diagnosed with colorectal cancer (CRC) from portal-phase abdominopelvic CT images. The study found that, although inferior to radiologists, the radiomics model was able to achieve substantial diagnostic performance when differentiating

Read More →

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

European Radiology March 2020

This study identified 236 patients from two cohorts who underwent surgery for ground-glass nodules (GGNs). The novel marginal features described, when combined with a radiomics model, could help to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) on preoperative CT scans. Key points Our novel marginal features could improve the existing radiomics model to

Read More →

Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification

European Radiology February 2020

The authors of this study evaluated the impact of utilizing digital breast tomosynthesis (DBT) and/or full-field digital mammography (FFDM), and different transfer learning strategies, on deep convolutional neural network (DCNN)-based mass classification for breast cancer. The authors found that integrating DBT and FFDM in DCNN training helps to enhance breast mass classification accuracy. Key points Transfer learning facilitates mass classification

Read More →

Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings

European Radiology January 2020

In this study, the authors sought to investigate the feasibility of a deep learning-based detection (DLD) system for multiclass legions on chest radiograph. The study included almost 16,000 chest radiographs collected from two tertiary hospitals, which were then used to develop a DLD system. The authors found that the DLD system showed potential for detections of lesions and pattern classification.

Read More →

Become A Member Today!

You will have access to a wide range of benefits that can help you advance your career and stay up-to-date with the latest developments in the field of radiology. These benefits include access to educational resources, networking opportunities with other professionals in the field, opportunities to participate in research projects and clinical trials, and access to the latest technologies and techniques. 

Check out our different membership options.

If you don’t find a fitting membership send us an email here.

①

Membership

for radiologists, radiology residents, professionals of allied sciences (including radiographers/radiological technologists, nuclear medicine physicians, medical physicists, and data scientists) & professionals of allied sciences in training residing within the boundaries of Europe

  • Reduced registration fees for ECR 1
  • Reduced fees for the European School of Radiology (ESOR) 2
  • Exclusive option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 4
  • Content e-mails for all ESR journals
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 11 /year

Yes! That is less than €1 per month.
②

Free membership

for radiologists, radiology residents or professionals of allied sciences engaged in practice, teaching or research residing outside Europe as well as individual qualified professionals with an interest in radiology and medical imaging who do not fulfil individual or all requirements for any other ESR membership category & former full members who have retired from all clinical practice
  • Reduced registration fees for ECR 1
  • Free electronic access to the journal European Radiology
  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

€ 0

The best things in life are free.
③

ESR Friends

For students, company representatives or hospital managers etc.

  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters

€ 0

Friendship doesn’t cost a thing.
Apply now

The membership type best fitting for you will be selected automatically during the application process.

Footnotes:

01

Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.