Machine learning with multi-site imaging data: an empirical study on the
impact of scanner effects
impact of scanner effects
File(s)
Accepted version
Author(s)
Glocker, Ben
Robinson, Robert
Castro, Daniel C
Dou, Qi
Konukoglu, Ender
Type
Conference Paper
Abstract
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.
Date Issued
2019-12-14
Date Acceptance
2019-10-01
Citation
2019
Publisher
NeurIPS
Copyright Statement
© 2019 The Author(s)
Sponsor
Commission of the European Communities
Identifier
http://arxiv.org/abs/1910.04597v1
Grant Number
H2020 - 757173
Source
Medical Imaging meets NeurIPS
Subjects
eess.IV
eess.IV
cs.CV
cs.LG
q-bio.NC
Notes
Presented at the Medical Imaging meets NeurIPS Workshop 2019
Publication Status
Published
Start Date
2019-12-14
Coverage Spatial
Vancouver, Canada