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

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

Abstract 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 detecti...

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Bibliographic Details
Main Authors: Michael Selle, Magdalena Kircher, Cornelia Schwennen, Christian Visscher, Klaus Jung
Format: Article
Language:English
Published: BMC 2024-02-01
Series:BMC Medical Informatics and Decision Making
Subjects:
CT scans
Outlier detection
Dimension reduction
Multiple co-inertia analysis
Bagplots
Online Access:https://doi.org/10.1186/s12911-024-02457-8

Internet

https://doi.org/10.1186/s12911-024-02457-8

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