Background With the ever-increasing amount of medical imaging data, the demand for
algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models
promise to aid in the crucial first step of disease detection. While previous studies have
thoroughly explored fairness in supervised models in healthcare, for UAD, this has so far
been unexplored. Methods In this study, we evaluated how dataset composition regarding
subgroups manifests in disparate performance of UAD models along multiple protected …

Background: With the ever-increasing amount of medical imaging data, the demand for
algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models
promise to aid in the crucial first step of disease detection. While previous studies have
thoroughly explored fairness in supervised models in healthcare, for UAD, this has so far
been unexplored. Methods: In this study, we evaluated how dataset composition regarding
subgroups manifests in disparate performance of UAD models along multiple protected …