
Proceedings Paper
The exploration machine: a novel method for analyzing high-dimensional data in computer-aided diagnosisFormat | Member Price | Non-Member Price |
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Paper Abstract
Purpose: To develop, test, and evaluate a novel unsupervised machine learning method for computer-aided diagnosis and
analysis of multidimensional data, such as biomedical imaging data. Methods: We introduce the Exploration Machine
(XOM) as a method for computing low-dimensional representations of high-dimensional observations. XOM systematically
inverts functional and structural components of topology-preserving mappings. By this trick, it can contribute to
both structure-preserving visualization and data clustering. We applied XOM to the analysis of whole-genome microarray
imaging data, comprising 2467 79-dimensional gene expression profiles of Saccharomyces cerevisiae, and to model-free
analysis of functional brain MRI data by unsupervised clustering. For both applications, we performed quantitative comparisons
to results obtained by established algorithms. Results: Genome data: Absolute (relative) Sammon error values
were 5.91·105 (1.00) for XOM, 6.50·105 (1.10) for Sammon's mapping, 6.56·105 (1.11) for PCA, and 7.24·105 (1.22) for
Self-Organizing Map (SOM). Computation times were 72, 216, 2, and 881 seconds for XOM, Sammon, PCA, and SOM,
respectively. - Functional MRI data: Areas under ROC curves for detection of task-related brain activation were 0.984 ±
0.03 for XOM, 0.983 ± 0.02 for Minimal-Free-Energy VQ, and 0.979 ± 0.02 for SOM. Conclusion: For both multidimensional
imaging applications, i.e. gene expression visualization and functional MRI clustering, XOM yields competitive
results when compared to established algorithms. Its surprising versatility to simultaneously contribute to dimensionality
reduction and data clustering qualifies XOM to serve as a useful novel method for the analysis of multidimensional data,
such as biomedical image data in computer-aided diagnosis.
Paper Details
Date Published: 27 February 2009
PDF: 7 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600G (27 February 2009); doi: 10.1117/12.813892
Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)
PDF: 7 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600G (27 February 2009); doi: 10.1117/12.813892
Show Author Affiliations
Axel Wismüller, Univ. of Rochester (United States)
Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)
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