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Bercea, C.-I. ; Puyol-Antón, E.* ; Wiestler, B.* ; Rueckert, D.* ; Schnabel, J.A. ; King, A.P.*

Bias in Unsupervised Anomaly Detection in Brain MRI.

In: (Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging). Berlin [u.a.]: Springer, 2023. 122-131 (Lect. Notes Comput. Sc. ; 14242 LNCS)
DOI
Unsupervised anomaly detection methods offer a promising and flexible alternative to supervised approaches, holding the potential to revolutionize medical scan analysis and enhance diagnostic performance. In the current landscape, it is commonly assumed that differences between a test case and the training distribution are attributed solely to pathological conditions, implying that any disparity indicates an anomaly. However, the presence of other potential sources of distributional shift, including scanner, age, sex, or race, is frequently overlooked. These shifts can significantly impact the accuracy of the anomaly detection task. Prominent instances of such failures have sparked concerns regarding the bias, credibility, and fairness of anomaly detection. This work presents a novel analysis of biases in unsupervised anomaly detection. By examining potential non-pathological distributional shifts between the training and testing distributions, we shed light on the extent of these biases and their influence on anomaly detection results. Moreover, this study examines the algorithmic limitations that arise due to biases, providing valuable insights into the challenges encountered by anomaly detection algorithms in accurately capturing the variability in the normative distribution. Here, we specifically investigate Alzheimer’s disease detection from brain MR imaging as a case study, revealing significant biases related to sex, race, and scanner variations that substantially impact the results. These findings align with the broader goal of improving the reliability, fairness, and effectiveness of anomaly detection.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Bias ; Fairness ; Unsupervised Anomaly Detection
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 14242 LNCS, Heft: , Seiten: 122-131 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute for Machine Learning in Biomed Imaging (IML)
Förderungen Helmholtz Association