ArticleMedical image analysis2022
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Liver cancer segmentator: Metadata-guided confidence scoring for reliable segmentation of colorectal liver metastases in CT.Computer methods and programs in biomedicine · 2026Article
- Label tree semantic losses for rich multi-class medical image segmentation.Frontiers in artificial intelligence · 2026Article
- Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation.Transactions on machine learning research · 2026Article
- Self-adaptive forward-forward network for anomaly detection and medical image analysis.Frontiers in radiology · 2026Article
- Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control.Journal of imaging informatics in medicine · 2025Article
- Jointly exploring client drift and catastrophic forgetting in dynamic learning.Scientific reports · 2025Article
- Uncertainty quantification via localized gradients for deep learning-based medical image assessments.Physics in medicine and biology · 2024Article
- Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data.Sensors (Basel, Switzerland) · 2024Article
- Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation.The journal of machine learning for biomedical imaging · 2024Article
- Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery.International journal of computer assisted radiology and surgery · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation. However, deep learning models are not trusted in the clinical routine due to failing silently on out-of-distribution (OOD) data. We propose a lightweight OOD detection method that leverages the Mahalanobis distance in the feature space and seamlessly integrates into state-of-the-art segmentation pipelines. The simple approach can even augment pre-trained models with clinically relevant uncertainty quantification. We validate our method across four chest CT distribution shifts and two magnetic resonance imaging applications, namely segmentation of the hippocampus and the prostate. Our results show that the proposed method effectively detects far- and near-OOD samples across all explored scenarios.
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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.