(Predictable) performance bias in unsupervised anomaly detectionResearch in context

(Predictable) performance bias in unsupervised anomaly detectionResearch in context

Summary: 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 fair...

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Bibliographic Details
Main Authors:	Felix Meissen, Svenja Breuer, Moritz Knolle, Alena Buyx, Ruth Müller, Georgios Kaissis, Benedikt Wiestler, Daniel Rückert
Format:	Article
Language:	English
Published:	Elsevier 2024-03-01
Series:	EBioMedicine
Subjects:	Artificial intelligence Machine learning Algorithmic bias Subgroup disparities Anomaly detection
Online Access:	http://www.sciencedirect.com/science/article/pii/S2352396424000379

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