Unreading Race: Purging Protected Features from Chest X-ray Embeddings
Tobias Weber Michael Ingrisch Bernd Bischl David Rügamer
Purpose: To analyze and remove protected feature effects in chest radiograph
embeddings of deep learning models.
  Materials and Methods: An orthogonalization is utilized to remove the
influence of protected features (e.g., age, sex, race) in chest radiograph
embeddings, ensuring feature-independent results. To validate the efficacy of
the approach, we retrospectively study the MIMIC and CheXpert datasets using
three pre-trained models, namely a supervised contrastive, a self-supervised
contrastive, and a baseline classifier model. Our statistical analysis involves
comparing the original versus the orthogonalized embeddings by estimating
protected feature influences and evaluating the ability to predict race, age,
or sex using the two types of embeddings.
  Results: Our experiments reveal a significant influence of protected features
on predictions of pathologies. Applying orthogonalization removes these feature
effects. Apart from removing any influence on pathology classification, while
maintaining competitive predictive performance, orthogonalized embeddings
further make it infeasible to directly predict protected attributes and
mitigate subgroup disparities.
  Conclusion: The presented work demonstrates the successful application and
evaluation of the orthogonalization technique in the domain of chest X-ray
classification.