Imperial College London
Portrait Picture

Dr Ben Glocker

Faculty of Engineering, Department of Computing

Professor in Machine Learning for Imaging
 
 
 
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Contact

 

+44 (0)20 7594 8334b.glocker Website CV

 
 
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Location

 

377Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Glocker:2019,
author = {Glocker, B and Robinson, R and Castro, DC and Dou, Q and Konukoglu, E},
publisher = {NeurIPS},
title = {Machine learning with multi-site imaging data: an empirical study on theimpact of scanner effects},
url = {http://arxiv.org/abs/1910.04597v1},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - This is an empirical study to investigate the impact of scanner effects when us-ing machine learning on multi-site neuroimaging data.  We utilize structural T1-weighted  brain  MRI  obtained  from  two  different  studies,  Cam-CAN  and  UKBiobank. For the purpose of our investigation, we construct a dataset consisting ofbrain scans from 592 age- and sex-matched individuals, 296 subjects from eachoriginal study. Our results demonstrate that even after careful pre-processing withstate-of-the-art neuroimaging pipelines a classifier can easily distinguish betweenthe origin of the data with very high accuracy. Our analysis on the example appli-cation of sex classification suggests that current approaches to harmonize data areunable to remove scanner-specific bias leading to overly optimistic performanceestimates and poor generalization. We conclude that multi-site data harmonizationremains an open challenge and particular care needs to be taken when using suchdata with advanced machine learning methods for predictive modelling.
AU  - Glocker,B
AU  - Robinson,R
AU  - Castro,DC
AU  - Dou,Q
AU  - Konukoglu,E
PB  - NeurIPS
PY  - 2019///
TI  - Machine learning with multi-site imaging data: an empirical study on theimpact of scanner effects
UR  - http://arxiv.org/abs/1910.04597v1
UR  - http://hdl.handle.net/10044/1/75012
ER  -
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