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Titel:

(Predictable) performance bias in unsupervised anomaly detection.

Dokumenttyp:
Journal Article
Autor(en):
Meissen, Felix; Breuer, Svenja; Knolle, Moritz; Buyx, Alena; Müller, Ruth; Kaissis, Georgios; Wiestler, Benedikt; Rückert, Daniel
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 mode...     »
Zeitschriftentitel:
EBioMedicine
Jahr:
2024
Band / Volume:
101
Volltext / DOI:
doi:10.1016/j.ebiom.2024.105002
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/38335791
TUM Einrichtung:
Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski); Institut für KI und Informatik in der Medizin (Prof. Rückert)
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