mlsanityData to 14 Oct 2026

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

arXiv 2308.03723 ↗First posted 7 Aug 2023cs.LG cs.AI cs.CVcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 7 Aug 2023.

Builds on

19 of 29 references.

References · 19

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7 more
  1. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 351
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2024Out-of-distribution Detection in Medical Image Analysis: A surveycited by 22
  2. 2024Robust Conformal Volume Estimation in 3D Medical Imagescited by 4
  3. 2025COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metricscited by 4
  4. 2024Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentationcited by 1
  5. 2025Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoderscited by 0
  6. 2026Domain Shift in Echocardiography: Interpretable Quantification and Prediction of Cross-Dataset Left Ventricular Segmentationcited by 0