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Back to main AI Blog

deep learning

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:

deep learningmachine learningmriradiomicsx-ray computed tomographyai newsinterviewmammographyCOVID-19breast cancerneural networksdiagnostic imagingworkflowtomographyMDCTdiagnosis

Latest posts

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 →

Changing the healthcare game through artificial intelligence

ESR September 2019

We recently spoke with Jörg Aumüller, who leads the Digital Health global marketing team at Siemens Healthineers. In our interview, we touched on the issue of growing medical data, how companies can stay ahead of legal and regulatory challenges, new roles and professions being created as a result of the introduction of AI tools and technology, and how these tools

Read More →

Looking outside the box: AI solves the Rubik’s cube, Africa as a new tech hub, and AI training in the UK

ESR August 2019

This week in artificial intelligence (AI) news, we take a look at AI solving the infamous Rubik’s cube, Africa’s future role in the global AI community, and the importance of radiological professionals being trained in AI in order to assess new tools and technologies coming to market. It’s one achievement to programme a computer to solve a Rubik’s cube, but

Read More →

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

European Radiology July 2019

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 →

Data control with AI: who’s in charge?

Melisande Rouger June 2019

With the increased use of artificial intelligence (AI) in every segment of life including healthcare, patient data is being collected massively and shared by different stakeholders. But who has control over it? The hospital, the patient, the equipment provider, the software developer, the state’s authorities? If no one knows who controls the data, what can happen? Peter van Ooijen, a

Read More →

Taking artificial intelligence out of the black box: An “interpretable” deep learning system for liver tumour diagnosis

European Radiology May 2019

Convolutional neural networks (CNN) have demonstrated the potential to become effective and accurate decision support tools for radiologists. A major barrier to clinical translation, however, is that the majority of such algorithms currently function like a “black box”. After training a CNN with a large set of input and output data, its internal layers are automatically adjusted to mathematically “map”

Read More →

Radiomics: a critical step towards integrated healthcare

Zuhir Bodalal April 2019

This article aims to bring together the various technological developments that have taken place in medical imaging analysis and highlight a potential path for the future. While the term “medical image analysis” has classically referred to radiological images (CT, MRI, PET, etc.), we must also remember that digitalization occurred much earlier in other diagnostic disciplines like pathology (with pathomics) and

Read More →

Should AI be seen as a threat or an opportunity in medical imaging?

European Radiology Experimental March 2019

The aim of this narrative review is to take a broader look at the application of Artificial Intelligence (AI), primarily in medical imaging. The authors define basic terms in AI, such as “machine learning” and “deep learning”, as well as provide an analysis on the integration of AI into radiology. Furthermore, the authors look at the increasing frequency of publications

Read More →

Can liver fibrosis be staged by deep learning techniques?

European Radiology March 2019

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 →

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 →

Changing the healthcare game through artificial intelligence

ESR September 2019

We recently spoke with Jörg Aumüller, who leads the Digital Health global marketing team at Siemens Healthineers. In our interview, we touched on the issue of growing medical data, how companies can stay ahead of legal and regulatory challenges, new roles and professions being created as a result of the introduction of AI tools and technology, and how these tools

Read More →

Looking outside the box: AI solves the Rubik’s cube, Africa as a new tech hub, and AI training in the UK

ESR August 2019

This week in artificial intelligence (AI) news, we take a look at AI solving the infamous Rubik’s cube, Africa’s future role in the global AI community, and the importance of radiological professionals being trained in AI in order to assess new tools and technologies coming to market. It’s one achievement to programme a computer to solve a Rubik’s cube, but

Read More →

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

European Radiology July 2019

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 →

Data control with AI: who’s in charge?

Melisande Rouger June 2019

With the increased use of artificial intelligence (AI) in every segment of life including healthcare, patient data is being collected massively and shared by different stakeholders. But who has control over it? The hospital, the patient, the equipment provider, the software developer, the state’s authorities? If no one knows who controls the data, what can happen? Peter van Ooijen, a

Read More →

Taking artificial intelligence out of the black box: An “interpretable” deep learning system for liver tumour diagnosis

European Radiology May 2019

Convolutional neural networks (CNN) have demonstrated the potential to become effective and accurate decision support tools for radiologists. A major barrier to clinical translation, however, is that the majority of such algorithms currently function like a “black box”. After training a CNN with a large set of input and output data, its internal layers are automatically adjusted to mathematically “map”

Read More →

Radiomics: a critical step towards integrated healthcare

Zuhir Bodalal April 2019

This article aims to bring together the various technological developments that have taken place in medical imaging analysis and highlight a potential path for the future. While the term “medical image analysis” has classically referred to radiological images (CT, MRI, PET, etc.), we must also remember that digitalization occurred much earlier in other diagnostic disciplines like pathology (with pathomics) and

Read More →

Should AI be seen as a threat or an opportunity in medical imaging?

European Radiology Experimental March 2019

The aim of this narrative review is to take a broader look at the application of Artificial Intelligence (AI), primarily in medical imaging. The authors define basic terms in AI, such as “machine learning” and “deep learning”, as well as provide an analysis on the integration of AI into radiology. Furthermore, the authors look at the increasing frequency of publications

Read More →

Can liver fibrosis be staged by deep learning techniques?

European Radiology March 2019

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.

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
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology 
  • Content e-mails for all ESR journals4
  • 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
  • Option to participate in the European Diploma. 3
  • Free electronic access to the journal European Radiology
  • Content e-mails for all 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 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.