Unreading Race: Purging Protected Features from Chest X-ray Embeddings. (arXiv:2311.01349v1 [cs.LG])

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 …

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