Bias in Unsupervised Anomaly Detection in Brain MRI

TL;DR

This paper analyzes biases in unsupervised brain MRI anomaly detection, highlighting how non-pathological factors like sex, race, and scanner differences affect accuracy and fairness.

Contribution

It provides a novel analysis of distributional biases in unsupervised anomaly detection, emphasizing their impact on medical imaging diagnostics.

Findings

Significant biases related to sex, race, and scanner variations were identified.

Biases substantially influence anomaly detection accuracy.

Insights into algorithmic limitations caused by these biases were discussed.

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 learning and capturing the entire range of variability present in the normative distribution. Through this analysis, we aim to enhance the understanding of these biases and pave the way for future improvements in the field. 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 in medical imaging.