Apr 6, 2023
*Bias in Unsupervised Anomaly Detection in Brain MRI
To be published soon…
Check our publications below for more details.
My research is focused on interpretable machine learning for anomaly detection.
UAD faces significant challenges tied to biases from different sources, including scanners, sex, and race. Future UAD models should maintain sensitivity to pathological shifts while minimizing sensitivity to non-pathological factors.
Cosmin I. Bercea, Esther Puyol-Antón, Benedikt Wiestler, Daniel Rückert, Julia A Schnabel, Andrew P. King
Moving beyond hyperintensity thresholding. This paper analyzes the challenges and outlines opportunities for advancing the field of unsupervised anomaly detection.
Cosmin I. Bercea, Benedikt Wiestler, Daniel Rückert, Julia A Schnabel