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Back to main AI Blog

thoracic radiography

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 cancerdiagnostic imagingneural networkstomographyradiographyMDCTworkflow

Latest posts

Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration

European Radiology November 2020

The purpose of this study was to evaluate the calibration of a deep learning (DL) model in a diagnostic cohort, as well as to improve the model’s calibration through recalibration procedures. The authors found that the calibration of the DL algorithm can be augmented through simple recalibration procedures, and improved calibration may enhance the interpretability and credibility of the model

Read More →

Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial

Stéphane Chauvie August 2020

Digital tomosynthesis (DTS) could be a realistic alternative to low dose CT for lung cancer screening, in particular for detecting nodules larger than 5 mm. In the past years, within SOS clinical trial we found that the probability of detecting lung cancer with the DTS in a population of heavy smokers older than 45 years was around 1%, a value

Read More →

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

European Radiology June 2020

In this study, the authors retrospectively collected 2,088 abnormal and 352 normal chest radiographs from two institutions in order to investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. This resulted in the matrix size 896 as having the highest performance for various sizes of abnormalities using different convolutional neural

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 →

Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration

European Radiology November 2020

The purpose of this study was to evaluate the calibration of a deep learning (DL) model in a diagnostic cohort, as well as to improve the model’s calibration through recalibration procedures. The authors found that the calibration of the DL algorithm can be augmented through simple recalibration procedures, and improved calibration may enhance the interpretability and credibility of the model

Read More →

Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial

Stéphane Chauvie August 2020

Digital tomosynthesis (DTS) could be a realistic alternative to low dose CT for lung cancer screening, in particular for detecting nodules larger than 5 mm. In the past years, within SOS clinical trial we found that the probability of detecting lung cancer with the DTS in a population of heavy smokers older than 45 years was around 1%, a value

Read More →

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

European Radiology June 2020

In this study, the authors retrospectively collected 2,088 abnormal and 352 normal chest radiographs from two institutions in order to investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. This resulted in the matrix size 896 as having the highest performance for various sizes of abnormalities using different convolutional neural

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.