EIBALL Session
EIBALL 19 - Imaging data harmonisation for reproducible imaging biomarkers and radiomics
Lectures
1
Chairperson's introduction
05:00Michelle Claire Williams, Edinburgh / UK
2
Why is data harmonisation important for imaging biomarkers and radiomics?
15:00Merel Huisman, Nijmegen / NL
3
Strategies for harmonisation of imaging data
15:00Irène Buvat, Orsay / FR
4
Artificial intelligence (AI) approaches to imaging data harmonisation
15:00Akshay Chaudhari, Stanford / US
5
Panel discussion: What are the key challenges for implementing data harmonisation in real-world clinical settings?
10:00Panel discussion: What are the key challenges for implementing data harmonisation in real-world clinical settings?
5 min
Chairperson's introduction
Michelle Claire Williams, Edinburgh / United Kingdom
15 min
Why is data harmonisation important for imaging biomarkers and radiomics?
Merel Huisman, Nijmegen / Netherlands
- To learn the key concepts and challenges in using imaging biomarkers and radiomics across multi-centre studies.
- To appreciate how variability in imaging protocols and acquisition impacts reproducibility and clinical utility of quantitative imaging biomarkers and radiomics.
- To understand the role of data harmonisation in enabling reliable, generalisable and clinically relevant analysis.
15 min
Strategies for harmonisation of imaging data
Irène Buvat, Orsay / France
- To learn the main methodological approaches for harmonising imaging data across scanners, protocols and institutions.
- To appreciate the advantages and limitations of preprocessing, standardisation and statistical correction techniques.
- To understand how harmonisation strategies can be applied in research and clinical practice.
15 min
Artificial intelligence (AI) approaches to imaging data harmonisation
Akshay Chaudhari, Stanford / United States
- To learn about current AI/ML-based techniques for harmonising imaging data.
- To appreciate the potential of AI/ML to address complex sources of variability in multi-centre imaging data.
- To understand how AI-driven harmonisation can support more accurate and robust imaging biomarker discovery and validation.
10 min
Panel discussion: What are the key challenges for implementing data harmonisation in real-world clinical settings?