  Purpose: High breast density is associated with reduced efficacy of
mammographic screening and increased risk of developing breast cancer. Accurate
and reliable automated density estimates can be used for direct risk prediction
and passing density related information to further predictive models. Expert
reader assessments of density show a strong relationship to cancer risk but
also inter-reader variation. The effect of label variability on model
performance is important when considering how to utilise automated methods for
both research and clinical purposes. Methods: We utilise subsets of images with
density labels to train a deep transfer learning model which is used to assess
how label variability affects the mapping from representation to prediction. We
then create two end-to-end deep learning models which allow us to investigate
the effect of label variability on the model representation formed. Results: We
show that the trained mappings from representations to labels are altered
considerably by the variability of reader scores. Training on labels with
distribution variation removed causes the Spearman rank correlation
coefficients to rise from $0.751\pm0.002$ to either $0.815\pm0.006$ when
averaging across readers or $0.844\pm0.002$ when averaging across images.
However, when we train different models to investigate the representation
effect we see little difference, with Spearman rank correlation coefficients of
$0.846\pm0.006$ and $0.850\pm0.006$ showing no statistically significant
difference in the quality of the model representation with regard to density
prediction. Conclusions: We show that the mapping between representation and
mammographic density prediction is significantly affected by label variability.
However, the effect of the label variability on the model representation is
limited.
