Imperial College London
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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{Piçarra:2023:10.1007/978-3-031-45249-9_19,
author = {Piçarra, C and Glocker, B},
doi = {10.1007/978-3-031-45249-9_19},
pages = {194--204},
title = {Analysing Race and Sex Bias in Brain Age Prediction},
url = {http://dx.doi.org/10.1007/978-3-031-45249-9_19},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Brain age prediction from MRI has become a popular imaging biomarker associated with a wide range of neuropathologies. The datasets used for training, however, are often skewed and imbalanced regarding demographics, potentially making brain age prediction models susceptible to bias. We analyse the commonly used ResNet-34 model by conducting a comprehensive subgroup performance analysis and feature inspection. The model is trained on 1,215 T1-weighted MRI scans from Cam-CAN and IXI, and tested on UK Biobank (n=42,786), split into six racial and biological sex subgroups. With the objective of comparing the performance between subgroups, measured by the absolute prediction error, we use a Kruskal-Wallis test followed by two post-hoc Conover-Iman tests to inspect bias across race and biological sex. To examine biases in the generated features, we use PCA for dimensionality reduction and employ two-sample Kolmogorov-Smirnov tests to identify distribution shifts among subgroups. Our results reveal statistically significant differences in predictive performance between Black and White, Black and Asian, and male and female subjects. Seven out of twelve pairwise comparisons show statistically significant differences in the feature distributions. Our findings call for further analysis of brain age prediction models.
AU  - Piçarra,C
AU  - Glocker,B
DO  - 10.1007/978-3-031-45249-9_19
EP  - 204
PY  - 2023///
SN  - 0302-9743
SP  - 194
TI  - Analysing Race and Sex Bias in Brain Age Prediction
UR  - http://dx.doi.org/10.1007/978-3-031-45249-9_19
ER  -
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