A Study of Demographic Bias in CNN-Based Brain MR Segmentation

TY - CHAP

T1 - A Study of Demographic Bias in CNN-Based Brain MR Segmentation

AU - Alzheimer’s Disease Neuroimaging Initiative

AU - Ioannou, Stefanos

AU - Chockler, Hana

AU - Hammers, Alexander

AU - King, Andrew P.

N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - Convolutional neural networks (CNNs) are increasingly being used to automate the segmentation of brain structures in magnetic resonance (MR) images for research studies. In other applications, CNN models have been shown to exhibit bias against certain demographic groups when they are under-represented in the training sets. In this work, we investigate whether CNN models for brain MR segmentation have the potential to contain sex or race bias when trained with imbalanced training sets. We train multiple instances of the FastSurferCNN model using different levels of sex imbalance in white subjects. We evaluate the performance of these models separately for white male and white female test sets to assess sex bias, and furthermore evaluate them on black male and black female test sets to assess potential racial bias. We find significant sex and race bias effects in segmentation model performance. The biases have a strong spatial component, with some brain regions exhibiting much stronger bias than others. Overall, our results suggest that race bias is more significant than sex bias. Our study demonstrates the importance of considering race and sex balance when forming training sets for CNN-based brain MR segmentation, to avoid maintaining or even exacerbating existing health inequalities through biased research study findings.

AB - Convolutional neural networks (CNNs) are increasingly being used to automate the segmentation of brain structures in magnetic resonance (MR) images for research studies. In other applications, CNN models have been shown to exhibit bias against certain demographic groups when they are under-represented in the training sets. In this work, we investigate whether CNN models for brain MR segmentation have the potential to contain sex or race bias when trained with imbalanced training sets. We train multiple instances of the FastSurferCNN model using different levels of sex imbalance in white subjects. We evaluate the performance of these models separately for white male and white female test sets to assess sex bias, and furthermore evaluate them on black male and black female test sets to assess potential racial bias. We find significant sex and race bias effects in segmentation model performance. The biases have a strong spatial component, with some brain regions exhibiting much stronger bias than others. Overall, our results suggest that race bias is more significant than sex bias. Our study demonstrates the importance of considering race and sex balance when forming training sets for CNN-based brain MR segmentation, to avoid maintaining or even exacerbating existing health inequalities through biased research study findings.

KW - Bias

KW - Brain

KW - Deep learning

KW - Fairness

KW - MR

UR - http://www.scopus.com/inward/record.url?scp=85141808686&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-17899-3_2

DO - 10.1007/978-3-031-17899-3_2

M3 - Conference paper

AN - SCOPUS:85141808686

SN - 9783031178986

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 13

EP - 22

BT - Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings

A2 - Abdulkadir, Ahmed

A2 - Bathula, Deepti R.

A2 - Dvornek, Nicha C.

A2 - Habes, Mohamad

A2 - Kia, Seyed Mostafa

A2 - Kumar, Vinod

A2 - Wolfers, Thomas

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022

Y2 - 18 September 2022 through 18 September 2022

ER -