Bias in Unsupervised Anomaly Detection in Brain MRI

Cosmin I. Bercea, Esther Puyol-Antón, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel, Andrew P. King

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationClinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging - 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, EPIMI 2023, Proceedings
EditorsStefan Wesarg, Cristina Oyarzun Laura, Esther Puyol Antón, Andrew P. King, John S.H. Baxter, Marius Erdt, Klaus Drechsler, Moti Freiman, Yufei Chen, Islem Rekik, Roy Eagleson, Aasa Feragen, Veronika Cheplygina, Melani Ganz-Benjaminsen, Enzo Ferrante, Ben Glocker, Daniel Moyer, Eikel Petersen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages122-131
Number of pages10
ISBN (Print)9783031452482
DOIs
StatePublished - 2023
Event12th International Workshop on Clinical Image-Based Procedures, CLIP 2023, 1st MICCAI Workshop on Fairness of AI in Medical Imaging, FAIMI 2023, held in conjunction with MICCAI 2023 and 2nd MICCAI Workshop on the Ethical and Philosophical Issues in Medical Imaging, EPIMI 2023 - Vancouver, Canada
Duration: 12 Oct 202312 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14242 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Workshop on Clinical Image-Based Procedures, CLIP 2023, 1st MICCAI Workshop on Fairness of AI in Medical Imaging, FAIMI 2023, held in conjunction with MICCAI 2023 and 2nd MICCAI Workshop on the Ethical and Philosophical Issues in Medical Imaging, EPIMI 2023
Country/TerritoryCanada
CityVancouver
Period12/10/2312/10/23

Keywords

  • Bias
  • Fairness
  • Unsupervised Anomaly Detection

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