An investigation into the impact of deep learning model choice on sex
  and race bias in cardiac MR segmentation
Tiarna Lee Esther Puyol-Antón Bram Ruijsink Keana Aitcheson Miaojing Shi Andrew P. King
In medical imaging, artificial intelligence (AI) is increasingly being used
to automate routine tasks. However, these algorithms can exhibit and exacerbate
biases which lead to disparate performances between protected groups. We
investigate the impact of model choice on how imbalances in subject sex and
race in training datasets affect AI-based cine cardiac magnetic resonance image
segmentation. We evaluate three convolutional neural network-based models and
one vision transformer model. We find significant sex bias in three of the four
models and racial bias in all of the models. However, the severity and nature
of the bias varies between the models, highlighting the importance of model
choice when attempting to train fair AI-based segmentation models for medical
imaging tasks.