Creating an AI Ecosystem for Multimodal Data Analysis in the MedGIFT Group


Henning Muller 1 , *

1 University of Applied Sciences Western Switzerland, Delémont, Switzerland

How to Cite: Muller H. Creating an AI Ecosystem for Multimodal Data Analysis in the MedGIFT Group, Iran J Radiol. 2019 ; 16(Special Issue):e99223. doi: 10.5812/iranjradiol.99223.


Iranian Journal of Radiology: 16 (Special Issue); e99223
Published Online: December 8, 2019
Article Type: Abstract
Received: October 30, 2019
Accepted: December 8, 2019


Background: The MedGIFT research group is on the border between medical sciences and computer science, namely medical image analysis and machine learning. The group was created in 2002 and has always had the aim to combine medical image data with other sources of information for medical decision support.

Objectives: Learning objectives include:

1. What is required to position a research group in medical image analysis?

2. How to get credibility in a multi-disciplinary domain?

3. How do research topics evolve over time and how to assure to stay relevant?

Outline: The talk will start with an overview of my personal profile and the history of how the MeGFT research group was started because this has had a strong influence on how the group evolved. Even though the initial name was kept, the topics in the group evolved much over time, mainly around medical topics. The development of systematic evaluation in scientific challenges has had a strong influence on the impact of our research group and thus, I will highlight the history of the ImageCLEF benchmark and how it has made datasets available for a large community and helped in further data-sharing efforts. The multidisciplinary nature of the research groups also helped in creating an ecosystem where researchers could flourish in several related disciplines. The close collaboration with several hospitals also assured that relevant topics are treated and in this respect also the final impact of the work.

Copyright © 2019, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.