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Abstract:Clinically deployed segmentation models are known to fail on data outside of their training distribution. As these models perform well on most cases, it is imperative to detect out-of-distribution (OOD) images at inference to protect against automation bias. This work applies the Mahalanobis distance post hoc to the bottleneck features of a Swin UNETR model that segments the liver on T1-weighted magnetic resonance imaging. By reducing the dimensions of the bottleneck features with principal component analysis, OOD images were detected with high performance and minimal computational load.
* This preprint has not undergone peer review or any post-submission
improvements or corrections. The Version of Record of this contribution will
be published in the Proceedings of Uncertainty for Safe Utilization of
Machine Learning in Medical Imaging (5th International Workshop) - Held in
conjunction with MICCAI 2023