ArticleThe journal of machine learning for biomedical imaging2024
Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation.
Article in The journal of machine learning for biomedical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Context-informed personalized segmentation from radiology reports: A fully automated framework for long-term liver tumor follow-up.Medical physics · 2026Article
- A Real-World Evaluation of Failure Detection for Liver CT Segmentation.medRxiv : the preprint server for health sciences · 2026Article
- Quality versus quantity of training datasets for artificial intelligence-based whole liver segmentation.medRxiv : the preprint server for health sciences · 2026Article
- Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation.Transactions on machine learning research · 2026Article
- Generative Modeling for Interpretable Anomaly Detection in Medical Imaging: Applications in Failure Detection and Data Curation.Bioengineering (Basel, Switzerland) · 2025Article
- Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation.The journal of machine learning for biomedical imaging · 2024Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions. While clinicians review the segmentations, these models tend to perform well in most instances, which could exacerbate automation bias. Therefore, detecting out-of-distribution images at inference is critical to warn the clinicians that the model likely failed. This work applied the Mahalanobis distance (MD) post hoc to the bottleneck features of four Swin UNETR and nnU-net models that segmented the liver on T1-weighted magnetic resonance imaging and computed tomography. By reducing the dimensions of the bottleneck features with either principal component analysis or uniform manifold approximation and projection, images the models failed on were detected with high performance and minimal computational load. In addition, this work explored a non-parametric alternative to the MD, a k-th nearest neighbors distance (KNN). KNN drastically improved scalability and performance over MD when both were applied to raw and average-pooled bottleneck features. Our code is available at https://github.com/mckellwoodland/dimen_reduce_mahal.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.