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

McKell Woodland, Nihil Patel, Mais Al Taie, Joshua P. Yung, Tucker J. Netherton +2 more
8/7/2023
cs.LGcs.AIcs.CV

Official code: mckellwoodland/dimen_reduce_mahal (4 stars, MIT, last commit 2025-05-20).

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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Code Implementations(1)

mckellwoodland/dimen_reduce_mahalOfficial100%
MIT

Official Repository for the MELBA paper entitled "Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation".

41Python, DockerfileAug 4, 20231 years agoMIT
k-nearest-neighborliver-cancermahalanobis-distancemedical-imaging-segmentationout-of-distribution-detection

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Cite this paper

@article{woodland2023dimensionality,
  title  = {Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation},
  author = {McKell Woodland and Nihil Patel and Mais Al Taie and Joshua P. Yung and Tucker J. Netherton and Ankit B. Patel and Kristy K. Brock},
  year   = {2023},
  eprint = {2308.03723},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.03723v2}
}

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