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Back to main AI Blog

CNN

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

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

European Radiology Experimental November 2019

The authors of this study aimed to assess the performance of a convolutional neural network (CNN) algorithm to register cross-sectional liver imaging series and its performance to manual image registration. The study included three hundred fourteen patients who underwent gadoxetate disodium-enhanced magnetic resonance imaging (MRI) and were retrospectively selected. Key points Image registration across series can improve lesion co-localization and

Read More →

Lost track of follow-up recommendations? Maybe AI could help you

Daniel Pinto dos Santos October 2019

By now, everybody knows that AI-based systems achieve impressive results in image analysis. But a recent blog post asked the question of what the next true killer apps could be [1]. Interestingly, the author raised the point that the next killer app may well be outside of the walls of the radiology department (i.e. further away from pixel analysis than

Read More →

AI power is real but muzzled by lack of data sharing and standardization

Melisande Rouger July 2019

AI adds a new dimension to brain and abdominal imaging in a number of clinical scenarios, but lack of data sharing as well as reproducibility and standardization issues must be addressed, top European radiologists explained during the ESR AI Premium event. Detecting the invisible and the visible In the brain, AI can help to detect things that are invisible to

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 →

Applying 3D CNN to CTA source images to detect ischemic stroke

European Radiology Experimental April 2019

In this study, the authors investigated how feasible it was to use 3D convolutional neural networks (CNN) to detect ischemic stroke from computed tomography angiography source images (CTA-SI). The study used CTA-SI from 60 randomly selected patients who had a suspected acute ischemic stroke of the middle cerebral artery; half of the patients were used in the neural network training,

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 →

Convolutional neural networks: an overview and application in radiology

Insights into Imaging February 2019

Numerous domains, including radiology, have shown interest in convolutional neural network (CNN) – a class of artificial neural networks that has become dominant in various computer vision tasks. It is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article

Read More →

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images

European Radiology Experimental November 2019

The authors of this study aimed to assess the performance of a convolutional neural network (CNN) algorithm to register cross-sectional liver imaging series and its performance to manual image registration. The study included three hundred fourteen patients who underwent gadoxetate disodium-enhanced magnetic resonance imaging (MRI) and were retrospectively selected. Key points Image registration across series can improve lesion co-localization and

Read More →

Lost track of follow-up recommendations? Maybe AI could help you

Daniel Pinto dos Santos October 2019

By now, everybody knows that AI-based systems achieve impressive results in image analysis. But a recent blog post asked the question of what the next true killer apps could be [1]. Interestingly, the author raised the point that the next killer app may well be outside of the walls of the radiology department (i.e. further away from pixel analysis than

Read More →

AI power is real but muzzled by lack of data sharing and standardization

Melisande Rouger July 2019

AI adds a new dimension to brain and abdominal imaging in a number of clinical scenarios, but lack of data sharing as well as reproducibility and standardization issues must be addressed, top European radiologists explained during the ESR AI Premium event. Detecting the invisible and the visible In the brain, AI can help to detect things that are invisible to

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 →

Applying 3D CNN to CTA source images to detect ischemic stroke

European Radiology Experimental April 2019

In this study, the authors investigated how feasible it was to use 3D convolutional neural networks (CNN) to detect ischemic stroke from computed tomography angiography source images (CTA-SI). The study used CTA-SI from 60 randomly selected patients who had a suspected acute ischemic stroke of the middle cerebral artery; half of the patients were used in the neural network training,

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 →

Convolutional neural networks: an overview and application in radiology

Insights into Imaging February 2019

Numerous domains, including radiology, have shown interest in convolutional neural network (CNN) – a class of artificial neural networks that has become dominant in various computer vision tasks. It is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article

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.