  In computer vision there has been significant research interest in assessing
potential demographic bias in deep learning models. One of the main causes of
such bias is imbalance in the training data. In medical imaging, where the
potential impact of bias is arguably much greater, there has been less
interest. In medical imaging pipelines, segmentation of structures of interest
plays an important role in estimating clinical biomarkers that are subsequently
used to inform patient management. Convolutional neural networks (CNNs) are
starting to be used to automate this process. We present the first systematic
study of the impact of training set imbalance on race and sex bias in CNN-based
segmentation. We focus on segmentation of the structures of the heart from
short axis cine cardiac magnetic resonance images, and train multiple CNN
segmentation models with different levels of race/sex imbalance. We find no
significant bias in the sex experiment but significant bias in two separate
race experiments, highlighting the need to consider adequate representation of
different demographic groups in health datasets.
