Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images

Background Unsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dimensional data after dimension reduction. Clustering and outlier detection may, h...

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Bibliographic Details
Published in:BMC medical informatics and decision making 2024-02, Vol.24 (1), p.49-13, Article 49
Main Authors: Selle, Michael, Kircher, Magdalena, Schwennen, Cornelia, Visscher, Christian, Jung, Klaus
Format: Article
Language:English
Subjects:
Algorithms
Bagplots
Cluster Analysis
Clustering
Computed tomography
CT imaging
CT scans
Data analysis
Data processing
Datasets
Dimension reduction
Embedding
Health Informatics
Humans
Image processing
Information Systems and Communication Service
Libraries
Liver
Machine learning
Management of Computing and Information Systems
Medical examination
Medical imaging
Medical research
Medicine
Medicine & Public Health
Methods
Multiple co-inertia analysis
Organs
Organs (Anatomy)
Outlier detection
Outliers (statistics)
Principal Component Analysis
Principal components analysis
Reduction
Robustness
Shape recognition
Tomography, X-Ray Computed
Visualization
Citations: Items that this one cites
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