(Predictable) performance bias in unsupervised anomaly detection.

ABSTRACT:

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 variables on three large-scale publicly available chest X-ray datasets. Our experiments were validated using two state-of-the-art UAD models for medical images. Finally, we introduced subgroup-AUROC (sAUROC), which aids in quantifying fairness in machine lear

SUBMITTER: Meissen F 

PROVIDER: S-EPMC10873649 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature