Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation

• URL: http://arxiv.org/abs/2308.03723v2
• Date: Thu, 19 Oct 2023 19:15:01 GMT
• Title: Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation
• Authors: McKell Woodland, Nihil Patel, Mais Al Taie, Joshua P. Yung, Tucker J. Netherton, Ankit B. Patel, and Kristy K. Brock
• Abstract summary: This work applies the Mahalanobis distance post hoc to the bottleneck features of a Swin UNETR model that segments the liver. OOD images were detected with high performance and minimal computational load.
• Score: 1.6182609133335621
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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