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International Day of Radiology (IDoR)

November 8, 2018
Worldwide

November 8th marks the day that Wilhelm Röntgen discovered the existence of X-rays in 1895. Since 2012, a joint initiative of ESR, RSNA and ACR celebrates this day as the INTERNATIONAL DAY OF RADIOLOGY. Let’s celebrate together!

This year, the IDoR theme is cardiac radiology. Cardiac imaging is a fast-growing subspecialty of diagnostic radiology that plays a huge part in the assessment and management of heart patients throughout the world. Cardiac radiologists – the experts in charge – supervise or perform imaging examinations, using technology such as computed tomography (CT) and magnetic resonance imaging (MRI), and then interpret the resulting images to diagnose and monitor a wide range of diseases of the heart.

For more information, please visit the IDoR website.

Big Data for Imaging
Radiomics, Deep Learning and Distributed Learning - a hands-on course


December 9-12, 2018
Maastricht/NL

This course on Big Data for Imaging is a unique opportunity to join a community of leading edge practitioners in the field of Artificial Intelligence for Medical Imaging. During this 4-days immersive course, you will be able to attend lectures and workshops from world-class experts in Radiomics, Deep Learning and Distributed Learning. You can also bring your own curated dataset with you (open source or anonymized and cleared by ethics). If requested, we will perform “data matching” for attendees to facilitate external cross validation. There will be ample opportunity to network with faculty members, other participants and companies.

Our starting point is an overview of the history of Medical Imaging Artificial Intelligence, we then discuss the success stories but also the pitfalls. Next, we will review the process from data acquisition, access to the DICOM objects, features extraction, machine learning (including new developments with Deep Learning) analysis and validation.

In the final part of the course, we will discuss the current challenges and directions of research in the field; in particular, the necessity of dealing with large annotated data sets, the FAIR principles and the distributed learning approach. The course will be divided into lectures during the morning and hands on assignments in the afternoon. Participants are encouraged to come with their data and we will organize (if possible) matching data for validation from other participants on the course.

> Programme
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