AI and Machine Learning in Cancer Imaging 3.0

Enhancing Healthcare through AI
30th June & 1st July 2023

In collaboration with the Champalimaud Foundation, Lisbon

Following our highly successful event in 2019 attended by over 400 delegates from over 30 countries, our 2023 in-person meeting aims to promote better understanding and application of AI and machine learning in cancer imaging, and provide a multidisciplinary forum for radiologists, technicians/ radiographers, scientists and industry partners to discuss and interact. The meeting is organised jointly by the Champalimaud Foundation and the International Cancer Imaging Society.

This meeting will continue to highlight real-world translation in the development of infrastructure/ ecosystem/ data pipeline, as well as illustrative clinical use cases emerging from clinical needs. Controversies and advances in AI and machine learning approaches will be presented and discussed. An industrial forum provides the opportunity to hear from scanner manufacturers, data science companies and health organisations to understand the development of tools for clinical benefit. The meeting will also feature presentations from the EU-funded ProCancer-I and EuCanImage projects. The content will be delivered by a panel of invited international expert faculty.

The cost to attend this two-day meeting is €80 for residents/ €100 for non-residents/ €150 to register onsite.

12.5 CPD credits will be awarded according to the CPD scheme of the Royal College of Radiologists. EACCME accreditation has been applied for.

Who should participate?

Radiologists, technicians/ radiographers, scientists, industrial partners and all who are interest in the growth and development of AI and machine learning in cancer imaging.

What are the learning objectives?

1. To review key developments in AI and machine techniques for cancer imaging

2. To appreciate the current controversies and development for AI and machine learning for cancer imaging

3. To understand the perspectives from industry and domain experts

4. To learn from exemplars of clinical use cases and large scale research projects