ROC curve

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:

Latest posts

Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging

In an attempt to determine whether deep learning with the convolutional neural networks (CNN) can be used for identifying parkinsonian disorder on MRI, the authors of this study trained the CNN to distinguish each parkinsonian disorder and then assessed the CNN’s performance. The levels of accuracy achieved confirmed that deep learning with CNN can discriminate parkinsonian disorders with high accuracy.

Read More →

Using radiomics for the preoperative estimation of pathological grade in bladder cancer tumors

The goal of this study was to develop and authenticate an MRI-based radiomics strategy for the preoperative estimation of pathological grade in bladder cancer (BCa) tumors. The obtained radiomic features were evaluated for diagnostic abilities using receiver operating characteristic (ROC) curve analysis and compared using the DeLong test. The authors then performed validation using 30 consecutive patients and were able

Read More →

Can liver fibrosis be staged by deep learning techniques?

This pilot study aims to investigate whether liver fibrosis can be staged by deep learning techniques based on CT images. It included CT examinations of patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. Some additional images for training data were generated by rotating or parallel shifting

Read More →

Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging

In an attempt to determine whether deep learning with the convolutional neural networks (CNN) can be used for identifying parkinsonian disorder on MRI, the authors of this study trained the CNN to distinguish each parkinsonian disorder and then assessed the CNN’s performance. The levels of accuracy achieved confirmed that deep learning with CNN can discriminate parkinsonian disorders with high accuracy.

Read More →

Using radiomics for the preoperative estimation of pathological grade in bladder cancer tumors

The goal of this study was to develop and authenticate an MRI-based radiomics strategy for the preoperative estimation of pathological grade in bladder cancer (BCa) tumors. The obtained radiomic features were evaluated for diagnostic abilities using receiver operating characteristic (ROC) curve analysis and compared using the DeLong test. The authors then performed validation using 30 consecutive patients and were able

Read More →

Can liver fibrosis be staged by deep learning techniques?

This pilot study aims to investigate whether liver fibrosis can be staged by deep learning techniques based on CT images. It included CT examinations of patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. Some additional images for training data were generated by rotating or parallel shifting

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.

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.

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.

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.

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.